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1. Bibliographic Information

Title: PokeeResearch: Effective Deep Research via Reinforcement Learning from AI Feedback and Robust Reasoning Scaffold
Authors: Yi Wan, Jiuqi Wang, Liam Li, Jinsong Liu, Ruihao Zhu, Zheqing Zhu
Affiliations: Pokee AI
Journal/Conference: This paper is a preprint available on arXiv. Preprints are research articles shared publicly before or during the formal peer-review process. The date "October 20, 2025" is a fictional future date from the paper.
Abstract: The paper introduces PokeeResearch-7B, a 7-billion parameter "deep research agent" designed to answer complex questions by using external tools. The authors identify key weaknesses in existing agents, such as poor information retrieval, unreliable performance metrics, and fragile tool usage. To solve these issues, they propose two main innovations: (1) A training method called Reinforcement Learning from AI Feedback (RLAIF) that uses an AI judge to provide reward signals for accuracy and faithfulness, avoiding the need for human annotation. (2) A "reasoning scaffold" that improves robustness through self-verification and allows the agent to recover from errors. The paper reports that PokeeResearch-7B achieves state-of-the-art results among similarly sized models on ten different benchmarks, demonstrating that intelligent design can lead to highly effective and efficient AI agents.
Original Source Link:
- Official Source: https://arxiv.org/abs/2510.15862v1
- PDF Link: https://arxiv.org/pdf/2510.15862v1.pdf

2. Executive Summary

Background & Motivation (Why):
- Modern Large Language Models (LLMs) are being transformed into deep research agents—systems that can break down complex questions, search for information using external tools (like a web search), and synthesize a comprehensive, evidence-based answer.
- However, current agents suffer from significant limitations. They often perform shallow retrieval, failing to find deep or nuanced information. Their training relies on simplistic metrics like F1 score, which poorly measure true factual accuracy and can be misleading. Furthermore, their ability to use tools is brittle; a single error, like a failed API call, can cause the entire research process to fail without any chance for recovery.
- This paper addresses these gaps by focusing on improving the robustness (ability to handle errors) and alignment (learning what humans actually consider a "good" answer) of these agents, particularly for smaller, more efficient models.
Main Contributions / Findings (What):
- A Novel Training Framework: The paper introduces a training pipeline based on Reinforcement Learning from AI Feedback (RLAIF). Instead of using simple text-overlap metrics or expensive human feedback, this method uses a powerful external LLM as a "judge" to score the agent's answers on dimensions like factual accuracy and adherence to instructions. This reward signal is then used to train the agent's policy.
- A Robust Reasoning Scaffold: The agent's workflow is enhanced with a chain-of-thought-driven multi-call reasoning scaffold. This includes a crucial self-verification step where the agent critically evaluates its own generated answer. If it finds a flaw, it can re-enter the research process to correct it, making the system more resilient to errors.
- State-of-the-Art Performance: The resulting model, PokeeResearch-7B, achieves the best performance among all 7B-parameter open-source research agents across ten challenging benchmarks. This finding strongly suggests that sophisticated training techniques and reasoning structures can produce highly capable agents without needing massive model sizes.

Foundational Concepts:
- Tool-Augmented Large Language Models (LLMs): These are LLMs that have been given the ability to use external "tools," such as a web search engine, a calculator, or a code interpreter. This allows them to overcome the limitations of their internal knowledge and perform complex, multi-step tasks.
- Deep Research Agent: A specific type of tool-augmented LLM designed for information-seeking tasks. It autonomously decomposes a query, gathers evidence from various sources, and synthesizes a grounded, verifiable answer.
- Reinforcement Learning (RL): A machine learning paradigm where an "agent" learns to make decisions by performing actions in an "environment" to maximize a cumulative "reward." In this context, the agent is the LLM, the actions are generating text or tool calls, and the reward signals how good the final answer is.
- Reinforcement Learning from AI Feedback (RLAIF): A variant of the popular Reinforcement Learning from Human Feedback (RLHF) technique. In RLAIF, the reward signal that guides the agent's learning is generated by another powerful AI model (an "AI judge") instead of by humans. This makes the process more scalable and less expensive.
- Chain-of-Thought (CoT): A prompting method that encourages an LLM to "think step-by-step." By generating intermediate reasoning steps before giving a final answer, the model can tackle more complex problems more reliably.
- On-Policy vs. Approximately On-Policy RL: In RL, a policy is the agent's strategy. On-policy algorithms (like REINFORCE and RLOO) update the policy using data generated from the most current version of that same policy. Algorithms like Proximal Policy Optimization (PPO) are often called approximately on-policy because they can reuse data from recent past policies and use techniques like clipping, which makes training more stable but can introduce a small bias.
Previous Works:
- Information Seeking Benchmarks: The paper notes an evolution in how agents are evaluated. Early datasets like Natural Questions (NQ) and TriviaQA tested simple fact retrieval. More advanced ones like HotpotQA and Musique required combining information from multiple sources (multi-hop reasoning). The latest benchmarks, such as GAIA, BrowseComp, and Humanity's Last Exam (HLE), introduce real-world complexity, dynamic web navigation, and multi-modal challenges that push the limits of current agents.
- Information Seeking Agents: Progress has been driven by large, proprietary models like DeepResearch (OpenAI) and Grok-3 (x.ai), but their closed nature limits scientific study. The open-source community has developed alternatives like R1-Searcher and WebThinker, but they often lag in performance. The paper highlights that training methods are a key area of research, with a shift from standard supervised fine-tuning (SFT) towards more adaptive RL approaches like those used in StepSearch and WebSailor.
Differentiation:
- PokeeResearch distinguishes itself from prior work by using RLAIF with the RLOO algorithm. The authors argue RLOO is a "true on-policy" algorithm that provides an unbiased gradient estimate, which they found to be more effective and stable than the more common PPO-based approaches.
- The core architectural innovation is the research-verification cycle. Unlike other agents that stop once an answer is generated, PokeeResearch adds a self-correction step, where the model critically reviews its own output and can initiate further research if needed. This directly tackles the problem of "brittle" agent behavior.
- The explicit use of an AI judge for semantic correctness as the reward signal is a move away from flawed lexical metrics like F1 Score and Exact Match, aligning the training objective more closely with the desired outcome of factual accuracy.

4. Methodology (Core Technology & Implementation)

The methodology of PokeeResearch centers on its unique workflow, training algorithm, and reward design.

Deep Research Workflow: The agent operates in a research-verification cycle.
1. Research Mode: The agent receives a user's question and begins a multi-turn process. In each turn, it can either:
  - Issue a tool call (e.g., web_search) by enclosing it in <tool_call> tags. The system executes the tool and returns the result. The agent is designed to be robust; it can try again with different tool calls if one fails.
  - Provide a final answer by enclosing it in $<answer>$ tags.
2. Verification Mode: Once an answer is generated, the agent switches to this mode. It reviews the entire conversation history (question, tool calls, tool responses, and its own answer) to judge if the answer is correct and complete.
  - If the verification is successful (CORRECT), the process ends.
  - If the verification fails (INCORRECT), the agent receives feedback on what was wrong and re-enters the Research Mode to refine its answer. This cycle leverages the known generation-verification gap in LLMs, where models are often better at identifying errors in existing text than they are at generating error-free text from scratch.
Tools and Training Pipeline:
- Tools: The agent is equipped with two primary tools for web interaction:
  - Web Searching Tool (Serper): Takes a query and returns a list of search results, including URLs and snippets, from Google.
  - Web Reading Tool (Jina Reader): Takes a URL and returns a concise summary of the webpage's content.
- Training Data: The agent is trained on the MiroRL-GenQA dataset, which contains complex questions requiring multi-step research.
- Training Algorithm (RLOO): The model's policy is fine-tuned using the REINFORCE Leave-One-Out (RLOO) algorithm. This is an on-policy RL algorithm designed to reduce variance. For a given prompt $x$ $x$ , the algorithm proceeds as follows:
  1. Sample $k$ different completions $\{ y^{(i)} \}_{i=1}^{k}$ from the current policy $\pi_{\theta}$ .
  2. For each completion $y^{(i)}$ , calculate a baseline $b_i$ by averaging the rewards of all other completions. This is the "leave-one-out" part. $b_i = \frac{1}{k-1} \sum_{j \neq i} R(x, y^{(j)})$
  3. Calculate the advantage $A_i$ for each completion, which is its reward minus the baseline. $A_i = R(x, y^{(i)}) - b_i$
  4. Update the policy parameters $\theta$ by taking a step in the direction of the average advantage-weighted gradient. $\theta \leftarrow \theta + \alpha \frac{1}{k} \sum_{i=1}^{k} A_i \nabla \log \pi_{\theta}(y^{(i)} | x)$
  - Symbol Explanation:
    - $\pi_{\theta}$ : The LLM policy (the model) parameterized by weights $\theta$ .
    - $x$ : The input prompt (the user's question).
    - $y^{(i)}$ : The i-th completion (the agent's entire trajectory of thoughts, tool calls, and answer) sampled from the policy.
    - $k$ : The number of completions sampled per prompt.
    - R(x, y): The reward function, which scores a completion $y$ .
    - $b_i$ : The leave-one-out baseline for the i-th sample.
    - $A_i$ : The advantage of the i-th sample over the baseline.
    - $\alpha$ : The learning rate (step size).
    - $\nabla \log \pi_{\theta}(\cdot)$ : The gradient of the log-probability of the completion, which indicates how to change the weights $\theta$ to make that completion more likely.
Reward Design: The paper critiques traditional metrics and advocates for AI-based feedback.

该图像是图3示意图，展示了AI反馈相较于传统词汇度量在评估模型答案时的优势，突出其能更准确捕捉语义和事实正确性，避免F1分数高但事实错误或语义正确却得分为零的情况。

As shown in Figure 3, traditional metrics can be flawed:
- F1 Score: Measures word overlap. It can give a misleadingly high score to a factually incorrect answer if it shares many words with the ground truth (e.g., wrong birth date).
- Exact Match (EM): A strict binary metric. It can unfairly penalize a semantically correct answer that is phrased differently or contains extra, correct information (e.g., "New York, U.S." vs. "U.S.").
- AI Feedback: An external LLM judge compares the agent's answer and the ground truth for semantic equivalence. This approach correctly identifies the factually incorrect answer as Score 0 and the semantically correct but lexically different answer as Score 1.
  
  For training, PokeeResearch uses a reward signal ( $R_{AI}$ ) from an AI judge, supplemented with a small format reward to encourage correct output structure.

5. Experimental Setup

Datasets: The model was evaluated on 10 popular benchmarks, using only the text-based questions:
- Single-hop/Factoid QA: Natural Questions (NQ), TriviaQA, PopQA.
- Multi-hop Reasoning QA: HotpotQA, 2WikiMultiHopQA, Musique.
- Complex/Web-based QA: Bamboogle (BAMB), GAIA, BrowseComp, Humanity's Last Exam (HLE). A total of 1,228 questions were sampled from these benchmarks for testing.
Evaluation Metrics: The primary evaluation metric is mean accuracy at 4 runs (mean@4).
1. Conceptual Definition: For each question, the agent is run four independent times. An external, powerful LLM (Gemini-Flash-2.5-Lite) acts as a judge to determine if the final answer from each run is correct. The accuracy for a single question is the fraction of the four runs that produced a correct answer. The final reported score is the average of this accuracy across all questions in a benchmark.
2. Mathematical Formula: $\mathrm{accuracy} \doteq \frac{\# \mathrm{research\ threads\ where\ the\ answer\ is\ correct}}{4}$
3. Symbol Explanation:
  - # research threads where the answer is correct: The number of successful runs (out of 4) for a given question.
    
    For context, the paper also defines the standard lexical metrics it chose not to use for its primary reward:
- F1 Score:
  1. Conceptual Definition: A metric that measures the balance between precision (what fraction of predicted words are relevant) and recall (what fraction of ground-truth words are predicted). It's a way to score partial credit based on token overlap.
  2. Mathematical Formula: $F_1(G, T) \doteq \frac{2 \cdot P \cdot R}{P + R} \quad \text{where} \quad P = \frac{|G \cap T|}{|G|} \quad \text{and} \quad R = \frac{|G \cap T|}{|T|}$
  3. Symbol Explanation:
    - $G$ : The set of tokens in the generated answer.
    - $T$ : The set of tokens in the ground-truth answer.
    - $P$ : Precision.
    - $R$ : Recall.
- Exact Match (EM):
  1. Conceptual Definition: A binary metric that gives a score of 1 if the normalized agent answer is identical to a normalized ground-truth answer, and 0 otherwise. It is very strict.
  2. Mathematical Formula: $\mathrm{EM} = \begin{cases} 1 & \text{if } \text{normalize}(\text{generated}) = \text{normalize}(\text{ground\_truth}) \\ 0 & \text{otherwise} \end{cases}$
  3. Symbol Explanation:
    - normalize(): A function that typically converts text to lowercase, removes punctuation, and standardizes whitespace.
Baselines: PokeeResearch was compared against five other 7B-parameter deep research agents: R1-Searcher, SearchR1, ZeroSearch, ASearcher, and DeepResearcher. For a fair comparison, all models (including PokeeResearch) use the Qwen2.5-7B model as their backbone.

6. Results & Analysis

Core Results: The experimental results demonstrate that PokeeResearch consistently outperforms all other 7B-scale baseline models across all ten benchmarks.

Figure 1: Performance on HLE, GAIA and BrowseComp among 7B-scale deep research models. 该图像是一张柱状图，展示了不同7B规模深度研究模型在HLE、GAIA和BrowseComp三个基准数据集上的性能比较。图中PokeeResearch模型在各项基准测试中表现优异，尤其在GAIA数据集上得分最高。

Figure 2: Performance on 7 QA Benchmarks among 7B-scale deep research models. 该图像是图表，展示了论文中7B规模深度研究模型在7个QA基准测试上的性能比较。不同模型以不同颜色区分，PokeeResearch整体表现优异，得分最高。

As shown in Figure 1 and Figure 2, PokeeResearch (the dark orange bar) achieves the highest score in every single category. The improvement is particularly notable on the more difficult benchmarks like GAIA, where it scores 37.6, significantly ahead of the next best baseline at 24.03.

The following tables, transcribed from the paper's Table 1, provide the detailed numerical results.

Performance on HLE, GAIA, and BrowseComp:

Method	HLE	GAIA	BrowseComp
R1searcher	5.4	8.3	1.0
SearchR1	13.0	18.7	0.4
ZeroSearch	8.6	9.9	1.4
ASearcher	13.8	22.1	3.2
DeepResearcher	6.0	24.03	1.8
PokeeResearch	15.0	37.6	6.0

Performance on QA Benchmarks:

Method	BAMB	2WIKI	TQ	NQ	POPQA	MUSIQUE	HOTPOTQA
R1searcher	63.2	61.4	77.2	59.6	51.8	35.8	62.4
SearchR1	67.8	62.8	81.0	67.6	59.6	33.2	63.2
ZeroSearch	51.4	33.6	61.6	48.2	38.0	19.0	32.4
ASearcher	68.8	69.2	85.2	71.2	58.2	35.8	71.0
DeepResearcher	71.0	58.8	82.2	60.2	55.2	26.8	56.6
PokeeResearch	78.2	73.4	89.8	76.0	63.2	36.6	71.4

Ablations / Parameter Sensitivity: The paper does not include a formal ablation study (e.g., removing the verification step to measure its impact numerically). However, it provides a qualitative example to illustrate the effectiveness of the self-verification mechanism.
- Question: "In one of Walter Scott's Waverley' novels what was The Heart of Midlothian?"
- First Attempt: The agent correctly identifies that "The Heart of Midlothian" refers to the Old Tolbooth prison but presents the information as a summary of the novel's plot.
- Verification Mode: The agent activates its verification step and critiques its own answer, noting: "it does not explicitly state that 'The Heart of Midlothian' is the title of the novel." It judges its own answer as INCORRECT.
- Second Attempt: The agent re-enters research mode. The new answer begins: "In Walter Scott's Waverley novel 'The Heart of Midlothian,' the title refers to the Old Tolbooth prison...". This directly addresses the flaw identified during verification. The agent then approves this answer as CORRECT. This example clearly shows the practical benefit of the research-verification cycle in catching and correcting subtle but important errors.

7. Conclusion & Reflections

Conclusion Summary: The paper successfully introduces PokeeResearch-7B, a highly effective deep research agent. Its success is attributed to two key design principles: (1) training with RLAIF using the RLOO algorithm, which optimizes directly for semantic correctness and factual accuracy using scalable AI-generated rewards, and (2) a robust reasoning scaffold that incorporates a self-verification loop, allowing the agent to recover from its own errors. The model's state-of-the-art performance across ten benchmarks demonstrates that thoughtful algorithm and architecture design can be more critical than simply increasing model size, paving the way for more efficient, reliable, and aligned AI research assistants.
Limitations & Future Work:
- Author-Stated: The authors aim for their work to inspire a "new generation of autonomous, verifiable, and human-aligned research agents," suggesting that continued work on self-correction and alignment is the path forward.
- Implicit Limitations:
  - The evaluation is limited to text-only benchmarks, whereas many real-world research tasks are multi-modal (involving images, tables, etc.).
  - Both the RLAIF training and the final evaluation rely on an external LLM as a judge. The performance is therefore sensitive to the capabilities and potential biases of this judge model.
  - The computational overhead of the research-verification cycle and sampling multiple trajectories for RLOO is not discussed. These could increase latency and cost compared to simpler approaches.
Personal Insights & Critique:
- This paper presents a compelling and well-executed piece of research. The focus on robust and efficient 7B-scale models is highly relevant as the field seeks to build practical, deployable systems rather than just ever-larger models.
- The most significant contribution is the explicit research-verification loop. This is a simple but powerful idea that mimics a human's natural workflow of drafting and then reviewing/editing. Formalizing this as part of the agent's architecture is a clear step towards more reliable autonomous systems.
- The choice of RLOO over the more common PPO is an interesting technical detail. The claim that it is a "true on-policy" algorithm with an unbiased gradient is strong, and the reported empirical success makes it a worthy alternative for researchers in this domain to consider.
- An open question is the scalability of the self-verification step. In this paper, the agent verifies its own response. Future work could explore a "constitutional" approach where a separate, potentially smaller and faster, specialized verification model critiques the primary generation model, which could be more efficient. Overall, PokeeResearch provides a strong blueprint for building more dependable and intelligent AI agents.