1. Bibliographic Information

1.1. Title

Training LLM Agents to Empower Humans

1.2. Authors

The paper was submitted by anonymous authors for double-blind review. Therefore, their specific identities, affiliations, and research backgrounds are not disclosed.

1.3. Journal/Conference

The paper is available on OpenReview, a platform commonly used for peer review by major computer science conferences. Given the topic and quality, it is likely intended for a top-tier conference in artificial intelligence or machine learning, such as NeurIPS (Conference on Neural Information Processing Systems) or ICML (International Conference on Machine Learning). The OpenReview forum allows for public discussion and review of the submission.

1.4. Publication Year

The metadata indicates a future publication date of October 8, 2025. This, combined with its "under double-blind review" status, confirms that this is a pre-publication version of a paper submitted for review, likely in late 2024 or early 2025.

1.5. Abstract

The abstract posits that a truly helpful assistive AI agent should know when to act and when to cede control to the human user, especially at critical decision points. Current methods for training agents, such as mimicking experts or using reinforcement learning with human feedback, often encourage the agent to complete tasks autonomously, which can be counterproductive. These methods also typically rely on expensive, explicit human feedback. The authors propose a novel, unsupervised fine-tuning approach for Large Language Model (LLM) agents based on maximizing the human's empowerment—their ability to influence future outcomes. This method, called Empower, requires only offline text data. The paper's efficacy is demonstrated through two main evaluations:

1. An 18-person user study where the Empower assistant was preferred 78% of the time over a strong baseline, showing a 31% higher suggestion acceptance rate and 38% fewer suggestions.
2. A simulated environment for code assistance, where Empower-trained agents increased a simulated human's success rate by an average of 192% over a standard fine-tuned baseline. The paper concludes that this empowerment objective provides a scalable framework for creating useful and aligned AI agents without needing additional human feedback or external rewards.

1.6. Original Source Link

The paper is available on OpenReview and can be accessed via the following links:

• Original Source Link: https://openreview.net/forum?id=W9oGZd4B8R
• PDF Link: https://openreview.net/pdf?id=W9oGZd4B8R The paper is currently under review and not yet officially published.

2. Executive Summary

2.1. Background & Motivation

The central problem addressed by this paper is a common frustration in human-AI collaboration, particularly with LLM-based coding assistants like GitHub Copilot. While these assistants are helpful for boilerplate or simple code, they often become unhelpful by generating large blocks of code that make incorrect assumptions about the user's ultimate goal. This forces the user to spend significant time debugging and correcting the AI's "overly helpful" suggestions, disrupting their workflow.

Existing methods for training assistive agents have key shortcomings:

• Behavioral Cloning (Mimicking Experts): Training a model to simply imitate expert human actions from a dataset doesn't solve the problem. The issue isn't that the AI's suggestions are unrealistic, but that they might be solving the wrong problem from the user's perspective.

• Reinforcement Learning from Human Feedback (RLHF): Methods like RLHF, which fine-tune models based on explicit user preferences (e.g., "upvotes" or "downvotes"), are expensive, time-consuming, and can lead to misaligned behaviors if the reward model is not perfectly specified.

• Asking Clarifying Questions: While agents can interrupt the user to ask for clarification, this breaks the user's concentration and can make the interaction feel burdensome.

  The paper's innovative idea is to reframe the goal of assistance. Instead of trying to infer the human's specific, private intention, the agent should aim to maximize the human's empowerment. Empowerment, in this context, is the human's ability to effectively and easily influence future outcomes. An agent that maximizes human empowerment would automate predictable, low-impact tasks (like writing boilerplate code) and stop at critical junctures where the human needs to make an important design decision. This creates a more natural and less intrusive form of assistance. Crucially, the authors propose that this empowerment objective can be estimated and optimized using only offline, unlabeled text data, offering a scalable and unsupervised alignment technique.

2.2. Main Contributions / Findings

The paper presents three primary contributions:

1. The Empower Method: The authors propose a practical and scalable algorithm for fine-tuning LLM agents. The method works by identifying the longest "predictable" continuation of a piece of text (as judged by an LLM's own likelihood estimates) and training the assistant to automatically complete that portion. This leaves the human at the point where their next action is most uncertain and therefore most impactful (i.e., they are empowered).

2. Validation in a Simulated Environment: The paper introduces a multi-turn code assistance environment using simulated humans (other powerful LLMs). In this setup, an assistant agent fine-tuned with Empower (a Llama-3.1-8B-Instruct model) more than doubled the problem-solving success rate (Pass@1) of a simulated human programmer (a Gemma-3-27B-it model) on challenging coding problems, achieving a 192% improvement over a standard Supervised Fine-Tuning (SFT) baseline.

3. Validation with a Real Human Study: An 18-person, double-blind user study was conducted to compare the Empower assistant against a strong baseline. The results strongly favored the Empower assistant:

   • User Preference: Participants preferred the Empower assistant 78% of the time.

   • Higher-Quality Suggestions: Suggestions from Empower had a 31% higher acceptance rate and participants deleted 26% fewer characters from accepted suggestions, indicating the suggestions were more useful.

   • Less Intrusive Assistance: The Empower assistant was more judicious, providing 38% fewer suggestions overall, which users preferred.

     The key finding is that empowerment is a powerful, unsupervised learning signal for aligning assistive agents. It allows for the creation of more helpful and less frustrating LLM assistants at scale, without the need for costly human feedback loops or explicit reward modeling.

3. Prerequisite Knowledge & Related Work

3.1. Foundational Concepts

To fully grasp this paper, it's essential to understand the following concepts:

• Large Language Models (LLMs): LLMs are deep learning models, typically based on the Transformer architecture, trained on vast amounts of text data. Their fundamental training objective is next-token prediction, where the model learns to predict the most probable next word or token given a sequence of preceding text. This process, known as self-supervised learning, endows them with a powerful understanding of language, grammar, and world knowledge. Fine-tuning is the process of further training a pre-trained LLM on a smaller, more specific dataset to adapt it for a particular task, such as code generation or conversation.

• Markov Decision Process (MDP): An MDP is a mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision-maker. It is formally defined by a tuple $(S, A, P, R, \gamma)$, where:

  • $S$: A set of possible states. In this paper, a state is the code text written so far.
  • $A$: A set of possible actions. An action could be the AI suggesting code or the human typing.
  • $P$: The state transition probability function, $P(s' | s, a)$, which gives the probability of transitioning to state $s'$ from state $s$ after taking action $a$.
  • $R$: A reward function, R(s, a, s'), which gives the immediate reward for a transition.
  • $\gamma$: A discount factor, which prioritizes immediate rewards over future ones. The goal in a standard MDP is to find a policy (a mapping from states to actions) that maximizes the cumulative discounted reward.

• Information Theory: This field of mathematics deals with quantifying information. Two key concepts are crucial for this paper:

  • Entropy: A measure of the uncertainty or "surprise" associated with a random variable. For a discrete random variable $X$ with probability mass function p(x), the entropy H(X) is: $ H(X) = - \sum_{x \in X} p(x) \log_2 p(x) $ A high entropy means the outcome is very unpredictable, while a low entropy means the outcome is nearly certain.
  • Mutual Information (MI): A measure of the mutual dependence between two random variables. It quantifies the "amount of information" obtained about one random variable by observing the other. The MI between $X$ and $Y$ is: $ I(X; Y) = H(X) - H(X|Y) = H(Y) - H(Y|X) $ Where $H(X|Y)$ is the conditional entropy of $X$ given $Y$. High MI means knowing one variable greatly reduces uncertainty about the other.

• Empowerment: Defined by Klyubin et al. (2005), empowerment is an intrinsic motivation principle for agents. It is formally the channel capacity between an agent's sequence of actions and the future state of the environment. Intuitively, it measures an agent's ability to influence its environment. An agent with high empowerment is in a state from which it can reliably reach many different future states. This paper adapts this concept to a collaborative setting, aiming to maximize the human's empowerment, not the AI's.

3.2. Previous Works

The paper positions its work in contrast to several established lines of research:

• Learning from Human Preferences: This is the dominant paradigm for aligning LLMs.

  • Reinforcement Learning from Human Feedback (RLHF): Popularized by Christiano et al. (2017) and used to train models like InstructGPT (Ouyang et al., 2022). The process involves:
    1. Collecting a dataset of human preferences between different model outputs.
    2. Training a "reward model" to predict which output a human would prefer.
    3. Using this reward model as the reward function in a reinforcement learning algorithm (like PPO) to fine-tune the LLM policy. This approach is effective but data-intensive and can suffer from mis-specification of the reward model.
  • Direct Preference Optimization (DPO): Proposed by Rafailov et al. (2024), DPO is a more stable and direct method. It bypasses the need for an explicit reward model and instead directly optimizes the LLM policy to satisfy the same preference data, treating the problem as a simple classification task. While more efficient than RLHF, it still requires a dataset of human preferences.

• Assistive Agents and Assistance Games: This framework, introduced by Hadfield-Menell et al. (2016), formally models human-robot collaboration. In an "assistance game," the AI agent knows the environment dynamics but does not know the human's reward function. The agent's task is to infer the human's goal from their actions and then act to help them achieve it. The Empower method can be seen as a specific type of assistance game where the AI doesn't try to infer a specific reward, but instead uses the general proxy objective of maximizing the human's empowerment.

• Empowerment in Reinforcement Learning: Prior work (Du et al., 2020; Myers et al., 2024) has explored using empowerment to guide robot assistants in simple grid-world or video game environments. Myers et al. (2024) introduced effective empowerment, a more computationally tractable version that this paper builds upon. The key innovation of the current paper is scaling this principle to the high-dimensional, complex domain of language and code generation.

3.3. Technological Evolution

The evolution of assistive coding agents can be seen in stages:

1. Early Autocomplete: Simple, pattern-based completion of variable names or keywords.
2. Pre-LLM ML Models: More sophisticated models that could suggest entire lines of code based on local context.
3. Base LLM Assistants: Large language models trained on code (e.g., early versions of Codex) could generate entire functions but were not "aligned" for helpful interaction.
4. Instruction-Tuned & RLHF-Tuned Assistants: Modern assistants like GitHub Copilot are fine-tuned on instructions and human feedback to be more conversational and follow user intent. However, this often leads to the "over-generation" problem.
5. Empowerment-Trained Assistants (This Paper): This paper proposes the next step, moving away from direct goal inference and explicit feedback towards an unsupervised alignment objective that focuses on the quality and timing of assistance.

3.4. Differentiation Analysis

Compared to the main methods in related work, this paper's approach is novel in several key ways:

• Unsupervised vs. Supervised: RLHF and DPO are supervised methods that require costly, explicit human preference labels. Empower is unsupervised, leveraging only offline text data (e.g., existing code repositories).
• Goal-Agnostic vs. Goal-Inference: Assistance games and RLHF-based methods typically try to infer the human's specific, latent goal or reward function. Empower is goal-agnostic; it helps the user by taking actions that are broadly useful (i.e., empowering) without needing to know their exact intention.
• Intrinsic vs. Extrinsic Objective: The learning signal for Empower is intrinsic—it is calculated from the model's own uncertainty about the data. This contrasts with extrinsic reward signals from human labelers or pre-defined task success metrics.
• Focus on "When" vs. "What": While other methods focus on generating the "correct" content, Empower critically addresses when to stop generating. It trains the assistant to be judicious, completing predictable parts and ceding control at decision points.

4. Methodology

4.1. Principles

The core principle of the paper is to train an assistive agent to make the human user more empowered. An empowered user is one who is at a state from which they can easily and effectively bring about a wide range of desired future states.

The intuition is that human-written text, like code, is a mix of predictable and unpredictable parts.

• Predictable parts: Boilerplate code, standard function calls, closing brackets. These are "low-empowerment" tasks for a human to write, as there are few meaningful choices. The agent should automate these.

• Unpredictable parts: Choosing an algorithm, naming a variable, defining a complex logic branch. These are "high-empowerment" moments, or critical decision points, where the human's choice has a large impact on the future of the code. The agent should stop and let the human make these decisions.

  By training the assistant to complete the predictable text, it brings the human directly to these high-empowerment decision points, saving them time and effort without making incorrect, high-level assumptions.

4.2. Core Methodology In-depth (Layer by Layer)

The methodology translates the abstract principle of empowerment into a concrete, scalable algorithm. This is done by first defining empowerment mathematically and then creating a practical, computable approximation.

4.2.1. Step 1: Formalizing the Interaction as an MDP

The authors model the human-assistant interaction as a Markov Decision Process (MDP) with two agents: a human $H$ and a robot (LLM agent) $R$.

• State ($s_t$): The program text written so far.
• LLM Agent Action ($a_t^{\mathbf{R}}$): The agent suggests a piece of text (a code completion) to append.
• Human Action ($a_t^{\mathbf{H}}$): The human has three choices:
  1. ACCEPT the suggestion and optionally append their own text.
  2. REJECT the suggestion and append their own text.
  3. FINISH the program.
• State Transition: The next state $s_{t+1}$ is the new program text, determined by the previous state $s_t$ and the joint actions of the agent and human. For example, if the human accepts a suggestion $a_t^{\mathbf{R}}$ and adds jejich own text $\ell$, the new state is $s_{t+1} = (s_t, a_t^{\mathbf{R}}, \ell)$.

4.2.2. Step 2: Defining Empowerment Mathematically

The paper starts with the canonical definition of empowerment from Klyubin et al. (2005), which is the channel capacity between a sequence of $n$ actions $\mathfrak{a}_{t}^{n}$ and the resulting state $\mathfrak{s}_{t+n}$: $$C\left(p\left(\mathfrak{s}_{t+n} \mid \mathfrak{a}_{t}^{n} ; \mathfrak{s}_{t}\right)\right) \triangleq \max _{p\left(a_{t}^{n} ; \mathfrak{s}_{t}\right)} I\left(\mathfrak{a}_{t}^{n} ; \mathfrak{s}_{t+n} \mid \mathfrak{s}_{t}\right)$$

• Explanation of Symbols:
  • $I(\cdot ; \cdot | \cdot)$ is the conditional mutual information.
  • $\mathfrak{a}_{t}^{n}$ is a random variable representing a sequence of $n$ actions starting from time $t$.
  • $\mathfrak{s}_{t+n}$ is a random variable for the state at time $t+n$.
  • $\mathfrak{s}_{t}$ is the known current state.
  • $\max_{p(a_t^n; \mathfrak{s}_t)}$ indicates that we are maximizing over all possible probability distributions of action sequences.
• Intuition: This formula measures the maximum amount of information an agent's actions can "inject" into the future state. In other words, it quantifies the degree of control an agent has over its future. However, this is computationally intractable, as it requires maximizing over all possible action sequence distributions.

4.2.3. Step 3: Using a Tractable Alternative: Effective Empowerment

To make this practical, the authors adopt the effective empowerment objective from Myers et al. (2024), which is more computationally feasible. They define the effective empowerment of the human at state $\ell_{1:t}$ (the text written so far) as: $$\mathcal{E}\left(\pi_{\mathbf{H}}, \ell_{1: t}\right) \triangleq I\left(\ell_{t+1}^{\mathbf{H}} ; \ell^{+} \mid \ell_{1: t}\right)$$

• Explanation of Symbols:
  • $\pi_{\mathbf{H}}$ is the human's policy (their way of choosing actions).
  • $\ell_{1:t}$ is the current state (text up to token $t$).
  • $\ell_{t+1}^{\mathbf{H}}$ is a random variable for the next token the human writes.
  • $\ell^{+}$ is a random variable for the future text that will be written.
• Intuition: This measures how much the human's immediate next action ($\ell_{t+1}^{\mathbf{H}}$) influences the long-term future of the text ($\ell^{+}$). A high value means the human's next token is a critical choice that significantly shapes what comes next.

4.2.4. Step 4: Approximating Effective Empowerment

Directly computing this mutual information is still difficult. The authors make a series of practical approximations. First, they use the property that mutual information is upper-bounded by entropy: $$I\left(\ell_{t+1}^{\mathbf{H}} ; \ell^{+} \mid \ell_{1: t}\right) = H\left(\ell_{t+1}^{\mathbf{H}} \mid \ell_{1: t}\right)-H\left(\ell_{t+1}^{\mathbf{H}} \mid \ell^{+}, \ell_{1: t}\right) \leq H\left(\ell_{t+1}^{\mathbf{H}} \mid \ell_{1: t}\right)$$

• Explanation: The mutual information is the reduction in uncertainty about the human's next token ($\ell_{t+1}^{\mathbf{H}}$) after observing the future ($\ell^{+}$). This reduction cannot be more than the initial uncertainty, which is the entropy $H(\ell_{t+1}^{\mathbf{H}} \mid \ell_{1:t})$.

  Next, they need to estimate this entropy. The true human policy $\pi_{\mathbf{H}}$ is unknown, so they use a pre-trained LLM, denoted as $\hat{\pi}$, as a proxy to estimate the likelihood of the human's actions. Given a single sample of the human's actual next token(s) from an offline dataset, they use a one-sample Monte Carlo estimate for the entropy, which is simply the negative log-likelihood: $$\hat{H}\left(\ell_{t+1}^{\mathbf{H}} \mid \ell_{1: t}\right) \approx-\log \hat{\pi}\left(\ell_{t+1}^{\mathbf{H}} \mid \ell_{1: t}\right)$$ Combining these, the approximate upper bound on the human's empowerment becomes: $$\mathcal{E}\left(\pi_{\mathbf{H}}, \ell_{1: t}\right) \lesssim-\log \hat{\pi}\left(\ell_{t+1}^{\mathbf{H}} \mid \ell_{1: t}\right)$$ A high negative log-likelihood (i.e., a low probability) implies the text is surprising and thus represents a high-empowerment action for the human.

4.2.5. Step 5: The Empower Algorithm

The final algorithm, Empower, uses this approximation to select training data. The goal is to train the assistant to complete text that is predictable (low empowerment) and stop just before the text becomes unpredictable (high empowerment).

Given a document $\ell_{1:T}$ from an offline dataset and a randomly sampled prefix $\ell_{1:t}$ as the current state, the algorithm finds the optimal completion length $i^*$ to train on: $$i^{*}=\arg \max _{i}\left\{i:-\log \hat{\pi}\left(\ell_{t+1: t+i} \mid \ell_{1: t}\right)<\eta\right\}$$

• Explanation of Symbols:

  • $i$ is the length of a potential completion.
  • $\ell_{t+1:t+i}$ is the actual suffix of length $i$ from the document.
  • $\hat{\pi}$ is a pre-trained LLM used as a likelihood estimator.
  • $\eta$ is a manually chosen threshold.

• Procedural Flow: The algorithm iterates through increasing completion lengths $i=1, 2, 3, \ldots$. For each length, it calculates the negative log-likelihood of the ground-truth suffix $\ell_{t+1:t+i}$ using the estimator LLM $\hat{\pi}$. It continues as long as this value is less than the threshold $\eta$. The optimal length $i^*$ is the longest length that satisfies this condition. The assistant is then fine-tuned on the prompt-completion pair $(\ell_{1:t}, \ell_{t+1:t+i^*})$.

  The following figure from the paper illustrates this process. The assistant finds the longest completion whose cumulative likelihood (which is inversely related to negative log-likelihood) stays above a threshold. This identifies the "obvious" part of the text, stopping right before a decision point.

  该图像是论文中展示的示意图，描述了基于赋能最大化的代码补全路径选择过程，展示了当前状态、真实后缀与可能备选后缀之间的分支决策和赋能得分比较。

The algorithm is also presented in pseudocode:

Algorithm 1: Logit Threshold Empowerment (Empower)
Input: A text document $\ell_{1: T}$ with sampled state $\ell_{1: t}$
Output: Empowering suggestion $\ell_{t+1: t+i}$, for state $\ell_{1: t}$
    for $i \in\{1 \ldots T\}$ do
        $\hat{H} \leftarrow-\log \pi\left(\ell_{t+1: t+i} \mid \ell_{1: t}\right)$
        if $\hatH > \eta$ then
            return $\ell_{t+1: t+i-1}$

This process is repeated for many documents and prefixes to create a fine-tuning dataset.

5. Experimental Setup

5.1. Datasets

• Training Data: The training dataset consists of 4,138 unique competitive programming questions from Codeforces. The solutions were not written by humans but were generated by the Gemma-3-27B-it model. This is a crucial detail, as the training is based on AI-generated attempts, not human expert traces. The dataset is not filtered for correctness, meaning it includes both successful and failed attempts.
• Evaluation Benchmark: The experiments are conducted on LiveCodeBench, a benchmark of competitive programming problems that is regularly updated to prevent contamination from model training data. The authors use 554 problems from release #6.

5.2. Evaluation Metrics

The authors propose three metrics to evaluate assistant performance:

1. Pass@1

   • Conceptual Definition: This metric measures the raw problem-solving success rate. It is the percentage of problems for which the final code, generated through the human-assistant interaction, passes all hidden test cases. A higher Pass@1 is better.
   • Mathematical Formula: $ \text{Pass@1} = \frac{\text{Number of problems solved successfully}}{\text{Total number of problems attempted}} $
   • Symbol Explanation: This is a simple ratio.

2. Acceptance Rate

   • Conceptual Definition: This measures how often the human (or simulated human) accepts the assistant's suggestions. It serves as a proxy for the perceived utility or relevance of the suggestions. A higher acceptance rate is generally desirable.
   • Mathematical Formula: $ \text{Acceptance Rate} = \frac{\text{Number of accepted suggestions}}{\text{Total number of suggestions offered}} $
   • Symbol Explanation: This is also a straightforward ratio.

3. Discounted Pass Rate (DPR)

   • Conceptual Definition: This is a novel and more holistic metric introduced by the authors. It aims to measure the true "helpfulness" of an assistant by balancing a successful outcome with the cognitive effort required from the human. A good assistant should not only lead to a correct solution but do so efficiently, minimizing the amount of text the human has to read (verify) and write.
   • Mathematical Formula: $ \text{DPR} = 1_{\text{Correct Solution}} \cdot \gamma^{\alpha \cdot \text{Tokens Read} + \beta \cdot \text{Tokens Written}} $
   • Symbol Explanation:
     • $1_{\text{Correct Solution}}$: An indicator function that is 1 if the final solution is correct and 0 otherwise.
     • $\gamma$: A discount factor less than 1 (set to 0.999). It penalizes longer interactions.
     • Tokens Read: The total number of tokens suggested by the assistant that the human had to read.
     • Tokens Written: The total number of tokens the human had to write themselves.
     • $\alpha$: A weight for the cost of reading/verifying tokens (set to 0.1).
     • $\beta$: A weight for the cost of writing tokens (set to 0.5). The authors set $\beta > \alpha$, reflecting the assumption that writing code is more difficult than reading it. A higher DPR indicates a more efficient and helpful assistant.

5.3. Baselines

The Empower method is compared against several representative baselines to demonstrate its superiority:

• SFT-N: An assistant model fine-tuned (SFT) on the next $N$ tokens of the human's writing from the training data. This baseline tests whether simply providing short, ground-truth completions is effective. The authors test SFT-10 and SFT-20.

• SFT-RAND: An assistant fine-tuned on a random number of tokens (from 1 to 30). This avoids biasing the model towards a fixed completion length.

• Base: The original, pre-trained instruction-tuned assistant model (e.g., Llama-3.1-8B-Instruct) with no additional fine-tuning.

• Base-N: The Base model, but with its suggestions manually capped at a maximum length of $N$ tokens. This baseline is important because it helps disentangle the effects of the Empower training from the simple heuristic of "shorter suggestions are better." The authors test Base-10 and Base-20.

  The assistant models used are Llama-3.1-8B-Instruct, Qwen3-8B, and Qwen3-14B. The simulated human is Gemma-3-27B-it or Llama-3.3-70B-Instruct.

6. Results & Analysis

6.1. Core Results Analysis

The paper's results are presented in two main parts: the simulated user experiments and the real human user study.

6.1.1. Simulated User Study Results

The simulated setup uses a powerful LLM (Gemma-3-27B-it) to act as a "human" programmer, deciding whether to accept, reject, or ignore the assistant's suggestions.

The following chart from the paper (Figure 2) shows the performance of different assistant models when assisting the Gemma-3-27B-it simulated human.

Analysis of Figure 2:

• Pass@1 (Success Rate): Across all three base models (Llama-3.1-8B, Qwen3-8B, Qwen3-14B), the Empower variant consistently achieves the highest Pass@1 rate. For instance, with Llama-3.1-8B, Empower reaches a Pass@1 of ~0.176, significantly outperforming the next best baseline (Base at ~0.064) and all SFT variants (which score between 0.062 and 0.070). This is a dramatic improvement and the source of the "192% increase" claim from the abstract (specifically from results in Table 1).

• Acceptance Rate: The results here are more nuanced. While Empower has a very high acceptance rate, the Base-10 baseline (which just gives short suggestions) is also highly accepted. This supports the hypothesis that users (or simulated users) are more likely to accept shorter suggestions.

• Discounted Pass Rate (DPR): This is where Empower truly shines. It consistently achieves the highest DPR. This is a crucial result because it shows that Empower is not just getting a high Pass@1 by chance or by overwhelming the user. It is achieving success efficiently. For example, while Base-10 has a high acceptance rate, its DPR is much lower than Empower's. This indicates that its short suggestions, while frequently accepted, are less helpful in progressing towards a correct solution than the more intelligently timed suggestions from Empower.

  These results strongly suggest that Empower learns to provide suggestions that are not only likely to be accepted but are also genuinely helpful for solving the task, validating the empowerment objective.

6.1.2. Real Human User Study Results

To confirm the simulated findings, the authors conducted a double-blind study with 18 human participants. They compared the Empower assistant (using Llama-3.1-8B-Instruct) against the strongest baseline from a pilot, Base-20.

The following chart from the paper (Figure 3) summarizes the user study results.

Analysis of Figure 3:

• Subjective Preference (Most Enjoy): A clear majority of participants—78%—reported that they would most enjoy using the Empower assistant in practice. This result is statistically significant (p=0.015), providing strong evidence of superior user experience.

• Suggestion Relevance (Most Relevant): 61% of users found the Empower assistant's suggestions more relevant. While this trend favors Empower, the result was not statistically significant (p=0.240), suggesting both assistants provided relevant suggestions to some degree.

• Quantitative Interaction (Accept Ratio): The Empower assistant's suggestions were accepted significantly more often (8.08% vs. 6.18% for Base-20, p=0.0002). This mirrors the simulated results and indicates the suggestions were more on-point.

• Post-Acceptance Editing (Characters Deleted): Participants deleted 26% fewer characters from Empower's suggestions after accepting them (9.56 vs. 12.91 for Base-20, p=0.0118). This is a powerful metric, showing that the Empower suggestions were not just accepted but were more "correct" and required less manual fixing.

• Assistant Judiciousness: The paper notes that Empower produced far fewer suggestions than the baseline (~208 vs. ~333 per user) and the suggestions were shorter (43.6 vs. 82.2 characters).

  Taken together, the human study confirms the core hypothesis: the Empower assistant is less intrusive, provides more useful suggestions, and leads to a more enjoyable and efficient user experience. It achieves this by completing the "obvious" parts and stopping, rather than aggressively trying to complete the entire task.

6.2. Data Presentation (Tables)

The paper includes detailed tables in its appendix. The following are the full results from Tables 1 and 2, which support the analysis in Figure 2.

The following are the results from Table 1 of the original paper:

Note: This table presents results with Llama-3.3-70B-Instruct as the simulated human. The 192% improvement claim in the abstract is derived from this table: comparing Llama3.1-8B Instruct with Empower (Pass@1 of 0.282) to its SFT-20 baseline (Pass@1 of 0.097), the improvement is $(0.282 - 0.097) / 0.097 \approx 191\%$.

The following are the results from Table 2 of the original paper:

Note: This table presents results with Gemma-3-27B-it as the simulated human and forms the basis for Figure 2. The results are consistent with Table 1, showing Empower as the superior method.

6.3. Ablation Studies / Parameter Analysis

The paper does not contain a formal ablation study section. However, the choice of baselines serves a similar purpose:

• By comparing Empower to SFT-N and SFT-RAND, the authors show that their method of choosing completion length is superior to simply training on fixed-length or random-length ground-truth completions.

• By comparing Empower to Base-N, the authors demonstrate that the performance gains are not merely due to producing shorter suggestions. The Empower suggestions are more strategically chosen, leading to a higher DPR even when acceptance rates are comparable.

  A key hyperparameter in the Empower method is the likelihood threshold $η$. The paper uses $η=0.32$ for the simulated experiments and $η=4$ for the human study. The paper does not provide an analysis of how sensitive the model's performance is to this parameter or a justification for the difference in values between the two studies. This is a potential area for future investigation.

7. Conclusion & Reflections

7.1. Conclusion Summary

This paper successfully demonstrates that training LLM agents to maximize human empowerment is a highly effective, scalable, and unsupervised strategy for creating better assistive agents. The core contribution is the Empower method, a practical algorithm that fine-tunes an LLM to complete predictable sequences of text, thereby saving human effort on boilerplate tasks and ceding control at critical, high-impact decision points.

The method's effectiveness is rigorously validated through both simulated experiments and a real-world human study. In simulations, Empower-trained agents dramatically increased the success rate of a simulated programmer. In the human study, participants overwhelmingly preferred the Empower assistant, finding it more enjoyable, its suggestions more useful, and its behavior less intrusive. The work provides a compelling proof-of-concept for "self-supervised alignment," where desirable agent behaviors are learned from intrinsic properties of offline data, bypassing the need for expensive and often problematic explicit human feedback.

7.2. Limitations & Future Work

The authors acknowledge two primary limitations:

1. Domain Specificity: All experiments were conducted in the domain of competitive programming. Real-world software development involves much larger codebases, different styles, and more complex dependencies. The effectiveness of the Empower method, particularly the reliability of the LLM likelihood estimator, may vary in these more general settings.

2. Likelihood Estimator Robustness: The method's success hinges on the quality of the LLM used to estimate the predictability of text ($\hat{\pi}$). A more robust estimator may be required for different or more complex domains.

   For future work, the authors suggest applying the empowerment framework to other domains, such as writing assistance, and to more agentic applications where an AI can autonomously perform actions that a human would predictably take.

7.3. Personal Insights & Critique

This paper is compelling and presents a very promising direction for AI alignment and human-AI interaction.

Strengths:

• Elegant and Practical Idea: The central concept of "automating the predictable" is intuitive, elegant, and avoids the fiendishly difficult problem of inferring a human's true intent. The Empower algorithm is a simple and clever implementation of this idea.
• Unsupervised and Scalable: The method's ability to work with only offline, unlabeled text is its greatest strength. It opens the door to aligning agents at a massive scale without the bottleneck of human-in-the-loop training.
• Strong, Multi-Faceted Evaluation: The combination of a large-scale simulated study and a carefully designed human study provides robust evidence. The introduction and use of the DPR metric is also a valuable contribution, offering a more nuanced way to measure assistant quality than Pass@1 or Acceptance Rate alone.

Potential Issues and Areas for Improvement:

• Reliance on Simulated Humans: A major assumption is that an LLM (like Gemma or Llama) is a good proxy for a human programmer. While LLMs can mimic human-like text generation, their decision-making process for accepting/rejecting code might differ significantly from a real human's. The positive human study results mitigate this concern, but the vast majority of the quantitative results rely on this simulation.
• Gap Between Theory and Practice: The mathematical justification relies on a chain of approximations: empowerment is approximated by effective empowerment, which is upper-bounded by entropy, which is then estimated with a one-sample negative log-likelihood from a proxy LLM. While this works well in practice, the theoretical grounding is not perfectly tight.
• Hyperparameter Sensitivity: The threshold $η$ is a critical hyperparameter. The paper uses vastly different values for the simulation ($η=0.32$) and the human study ($η=4$) without explanation. This lack of analysis on how to choose $η$ and how sensitive the results are to it is a significant omission. A principled way to set this threshold, or a method that is robust to its value, would make the approach much more practical.
• Risk of Self-Empowerment: The authors briefly mention and dismiss the risk of an agent becoming power-seeking by maximizing its own empowerment. While their method focuses on human empowerment, the broader idea of using empowerment as a driving objective for AI agents warrants careful consideration of this potential failure mode in less constrained settings.

Inspirations and Future Value: This paper's most significant contribution may be its philosophical shift. It suggests that alignment might not always require teaching an AI what we want, but rather teaching it to understand where its help is and is not needed. The concept of "self-supervised alignment" is extremely powerful. It could be applied to many other areas of human-AI collaboration, creating AI systems that are deferential, respect human autonomy, and integrate more seamlessly into human workflows. This work is a strong step towards building AI that is not just capable, but truly helpful.