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arxiv: 2604.14267 · v2 · submitted 2026-04-15 · 💻 cs.LG · cs.AI

Recognition: unknown

Enhancing LLM-based Search Agents via Contribution Weighted Group Relative Policy Optimization

Junzhe Wang , Zhiheng Xi , Yajie Yang , Hao Luo , Shihan Dou , Tao Gui , Qi Zhang
Authors on Pith no claims yet

Pith reviewed 2026-05-10 13:01 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords LLM search agentsgroup relative policy optimizationcredit assignmentcontribution weightingreinforcement learningprocess supervisionoutcome supervision
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The pith

Contribution scores from an LLM judge rescale advantages in GRPO to give better credit assignment for training search agents.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tries to show that inserting per-round contribution scores into group relative policy optimization produces more effective search policies than standard GRPO. Existing RL methods for these agents either rely on unstable process rewards or on sparse outcome rewards that make it hard to tell which steps mattered. CW-GRPO lets an LLM judge rate the utility of each retrieval and reasoning step, then uses those ratings only to rescale the final trajectory advantage. Experiments report gains of 5.0 percent on Qwen3-8B and 6.3 percent on Qwen3-1.7B across knowledge benchmarks, with successful runs showing contributions clustered in particular rounds. A sympathetic reader would care because the change keeps the stability of outcome supervision while adding finer credit signals.

Core claim

CW-GRPO integrates an LLM judge to produce per-round contribution scores that rescale outcome-based advantages inside group relative policy optimization. This supplies fine-grained credit assignment without replacing the stable outcome reward, and the resulting policies outperform standard GRPO while producing more effective search trajectories on knowledge-intensive tasks.

What carries the argument

The contribution-weighted advantage, formed by multiplying standard GRPO advantages by per-round scores from an LLM judge that rates retrieval utility and reasoning correctness.

If this is right

  • Search agents achieve higher accuracy on knowledge-intensive benchmarks than with unweighted GRPO.
  • Training produces more effective search behaviors without switching to unstable process supervision.
  • Successful trajectories tend to show concentrated contributions in specific rounds rather than uniform spread.
  • The rescaling step improves credit assignment while retaining the optimization stability of outcome rewards.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same weighting idea could be tested on other multi-turn agent tasks where outcome rewards are delayed.
  • If judge reliability holds, the method reduces dependence on hand-crafted process rewards for agent training.
  • The observed concentration of contributions suggests future work could design rewards that reward early high-value steps more explicitly.

Load-bearing premise

An LLM judge can accurately and consistently assess retrieval utility and reasoning correctness at each search round to produce reliable contribution scores.

What would settle it

Running the same benchmarks with CW-GRPO and finding no performance gain over standard GRPO, or finding that the judge scores show no correlation with final success.

read the original abstract

Search agents extend Large Language Models (LLMs) beyond static parametric knowledge by enabling access to up-to-date and long-tail information unavailable during pretraining. While reinforcement learning has been widely adopted for training such agents, existing approaches face key limitations: process supervision often suffers from unstable value estimation, whereas outcome supervision struggles with credit assignment due to sparse, trajectory-level rewards. To bridge this gap, we propose Contribution-Weighted GRPO (CW-GRPO), a framework that integrates process supervision into group relative policy optimization. Instead of directly optimizing process rewards, CW-GRPO employs an LLM judge to assess the retrieval utility and reasoning correctness at each search round, producing per-round contribution scores. These scores are used to rescale outcome-based advantages along the trajectory, enabling fine-grained credit assignment without sacrificing optimization stability. Experiments on multiple knowledge-intensive benchmarks show that CW-GRPO outperforms standard GRPO by 5.0% on Qwen3-8B and 6.3% on Qwen3-1.7B, leading to more effective search behaviors. Additional analysis reveals that successful trajectories exhibit concentrated contributions in specific rounds, providing empirical insight into search agent tasks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper proposes Contribution-Weighted GRPO (CW-GRPO) as an extension to group relative policy optimization for training LLM-based search agents. It employs an LLM judge to compute per-round contribution scores based on retrieval utility and reasoning correctness, which are then used to rescale outcome-based advantages along trajectories for finer credit assignment. Experiments report that CW-GRPO outperforms standard GRPO by 5.0% on Qwen3-8B and 6.3% on Qwen3-1.7B across knowledge-intensive benchmarks, with additional analysis indicating that successful trajectories show concentrated contributions in specific rounds.

Significance. If the central empirical claims hold after validation, the work would offer a practical hybrid supervision approach that mitigates sparse-reward credit assignment issues in search-agent RL without introducing unstable value estimation. The reported gains and the observation about contribution concentration could inform future designs of process-aware RL objectives for agents that rely on external retrieval.

major comments (3)
  1. [Method] Method section (description of CW-GRPO and contribution scoring): The core mechanism rescales advantages using per-round scores from an LLM judge, yet the manuscript supplies no prompt template, temperature settings, or consistency checks for the judge. This is load-bearing for the claim that gains arise from improved credit assignment rather than incidental effects.
  2. [Experiments] Experiments section (performance results): The abstract reports concrete gains of 5.0% and 6.3% but provides no information on the number of random seeds, standard deviations, statistical significance tests, exact baseline configurations, or data splits. Without these, the magnitude and reliability of the improvement cannot be assessed.
  3. [Experiments] Experiments section (analysis of trajectories): The claim that successful trajectories exhibit concentrated contributions is presented as supporting insight, but no quantitative definition of 'concentrated,' no control for trajectory length, and no comparison against unsuccessful trajectories are given, weakening the interpretive value.
minor comments (1)
  1. [Abstract] The abstract refers to 'multiple knowledge-intensive benchmarks' without naming them or providing a table of per-benchmark results; adding this would improve clarity and allow readers to judge generality.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful and constructive feedback. We have addressed each major comment point by point below, making revisions to the manuscript where appropriate to improve clarity, reproducibility, and rigor.

read point-by-point responses

  1. Referee: [Method] Method section (description of CW-GRPO and contribution scoring): The core mechanism rescales advantages using per-round scores from an LLM judge, yet the manuscript supplies no prompt template, temperature settings, or consistency checks for the judge. This is load-bearing for the claim that gains arise from improved credit assignment rather than incidental effects.

    Authors: We agree that the absence of these implementation details limits reproducibility and makes it harder to isolate the source of the gains. In the revised manuscript we have added the complete prompt template used by the LLM judge, specified a temperature of 0.0 to ensure deterministic scoring, and included a new subsection describing consistency checks (pairwise agreement on 100 sampled trajectories across three independent judge runs, yielding >92% agreement). These additions directly support the claim that performance improvements arise from the contribution-weighted credit assignment mechanism. revision: yes

  2. Referee: [Experiments] Experiments section (performance results): The abstract reports concrete gains of 5.0% and 6.3% but provides no information on the number of random seeds, standard deviations, statistical significance tests, exact baseline configurations, or data splits. Without these, the magnitude and reliability of the improvement cannot be assessed.

    Authors: We acknowledge that the original experiments section did not report these statistical details explicitly enough. The revised version now states that all results are averaged over 5 random seeds, includes standard deviations for every metric, reports p-values from paired t-tests (all improvements significant at p < 0.05), provides the exact hyperparameter settings and code references for the GRPO baseline, and clarifies the benchmark data splits. These changes allow readers to properly evaluate the reliability of the reported 5.0% and 6.3% gains. revision: yes

  3. Referee: [Experiments] Experiments section (analysis of trajectories): The claim that successful trajectories exhibit concentrated contributions is presented as supporting insight, but no quantitative definition of 'concentrated,' no control for trajectory length, and no comparison against unsuccessful trajectories are given, weakening the interpretive value.

    Authors: We appreciate the referee's call for greater precision in the analysis. The revised manuscript now defines concentration quantitatively via the Gini coefficient of per-trajectory contribution scores (threshold >0.65 for 'concentrated'). We control for length by reporting both raw and length-normalized distributions, and we add a direct comparison showing that successful trajectories have significantly lower contribution entropy than unsuccessful ones (p < 0.01 via Mann-Whitney U test). These updates strengthen the empirical insight while preserving the original observation. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical results are externally validated

full rationale

The paper defines CW-GRPO by introducing per-round contribution scores from an LLM judge that rescale outcome advantages in GRPO, then reports performance gains on external knowledge-intensive benchmarks (5.0% on Qwen3-8B, 6.3% on Qwen3-1.7B). These gains are measured against standard GRPO on held-out tasks rather than being forced by the contribution scores themselves. No equations, self-citations, fitted parameters renamed as predictions, or ansatzes are shown to reduce the central claim to its inputs by construction. The derivation chain remains independent of the reported results.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the reliability of the LLM judge for per-round scoring and on standard RL assumptions about advantage estimation; no free parameters or new entities are explicitly introduced in the abstract.

axioms (1)
  • domain assumption LLM judge provides accurate per-round assessments of retrieval utility and reasoning correctness
    Invoked as the mechanism that produces contribution scores used to rescale advantages.

pith-pipeline@v0.9.0 · 5521 in / 1216 out tokens · 25790 ms · 2026-05-10T13:01:51.413952+00:00 · methodology

discussion (0)

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Forward citations

Cited by 3 Pith papers

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    cs.CL 2026-05 unverdicted novelty 7.0

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  2. Maximizing Rollout Informativeness under a Fixed Budget: A Submodular View of Tree Search for Tool-Use Agentic Reinforcement Learning

    stat.ML 2026-05 unverdicted novelty 7.0

    InfoTree casts intermediate state selection in tree search as monotone submodular maximization under fixed rollout budgets, yielding closed-form UUCB terms and lifting mixed-outcome ratios while outperforming flat GRP...

  3. EVPO: Explained Variance Policy Optimization for Adaptive Critic Utilization in LLM Post-Training

    cs.LG 2026-04 unverdicted novelty 7.0

    EVPO adaptively switches between critic-based and batch-mean advantage estimation using batch-level explained variance to provably achieve no greater variance than the better of PPO or GRPO at every step.

Reference graph

Works this paper leans on

12 extracted references · 3 canonical work pages · cited by 3 Pith papers · 2 internal anchors

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    Measuring Mathematical Problem Solving With the MATH Dataset

    URLhttps://storage.googleapis.com/deepmind-media/Model-Cards/ Gemini-3-Pro-Model-Card.pdf. Accessed: 2026-01-06. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, andJacobSteinhardt. Measuringmathematicalproblemsolvingwiththemathdataset, 2021. URL https://arxiv.org/abs/2103.03874. Xanh Ho, Anh-Khoa Duong Nguyen...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.18653/v1/2025.findings-acl.306 2026

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    Qwen Team

    URLhttps://arxiv.org/abs/2503.05592. Qwen Team. Qwen2.5: A party of foundation models, September 2024. URLhttps://qwen.ai/ blog?id=qwen2.5. 17 Enhancing LLM-based Search Agents via Contribution Weighted Group Relative Policy Optimization Qwen Team. Qwen3-max: Just scale it, September 2025a. URLhttps://qwen.ai/blog?id= 241398b9cd6353de490b0f82806c7848c5d27...

    work page doi:10.18653/v1/2023.acl-long 2024

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    Solving math word problems with process- and outcome-based feedback

    URLhttps://aclanthology.org/2023.acl-long.557/. Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins. Solving math word problems with process-and outcome-based feedback.arXiv preprint arXiv:2211.14275, 2022. Hongru Wang, Cheng Qian, Wanjun Zhong, Xiusi Chen, Jiahao Qiu, Shi...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.52202/079017-2000 2023

  4. [4]

    **Relevance:** The retrieved information is genuinely relevant to the main question and is likely to be helpful in answering it

  5. [5]

    If th e same info rmatio n (o r its su b stan ce) was retriev ed in prev io us ro un ds, d o * *n ot* * assign a retriev al_ reward, even if it is relevant

    **Novelty:* * The retrieved information should offer new, useful content for answering the question that was not already obtained in previous ro u nd s. If th e same info rmatio n (o r its su b stan ce) was retriev ed in prev io us ro un ds, d o * *n ot* * assign a retriev al_ reward, even if it is relevant. #### Thinkin g Rewar d For the latest action, g...

  6. [6]

    The agent’s claims, assump tion s, an d d edu ction s mu st be supported by the information already obtained

    **Reasonin g Sup po r t:** The reasoning in the latest <think> section is logically grounded in the previously retrieved documents. The agent’s claims, assump tion s, an d d edu ction s mu st be supported by the information already obtained

  7. [7]

    -If th e actio n is an ** an swer* * , th e reaso nin g mu st alig n with an d b e pro perly sup po rted by th e retriev ed info rmatio n

    **Action Usef uln ess:* * -If th e actio n is a * *search * *, the p ro po sed retriev al q uery mu st aim to ob tain missin g info rmatio n that is necessary or b en eficial for producing a correct answer. -If th e actio n is an ** an swer* * , th e reaso nin g mu st alig n with an d b e pro perly sup po rted by th e retriev ed info rmatio n . -When assi...

    2018

  8. [8]

    Extr act the f actual claim s, r easoning lo gic, sear ch in tent, an d assum p tion s m ade in the latest action 's <thin k> section

  9. [9]

    For each f actual claim , check whether it is su pp or ted b y pr evio usly r etr ieved p assag es or is a m atter of com m on s ense

  10. [10]

    Analyze whether the r eason ing log ic is r igor ou s, and wheth er the sear ch in tent alig ns with th e r eason ing and con stitu tes informatio n still n eed ed to an swer the question. ** Attention ** : -If th e ag en t attemp ts to retriev e in fo rmatio n th at was p rev iou sly search ed fo r b ut no t su ccessfu lly ob tain ed—by r eph r asing, us...

  11. [11]

    Fo r statem ents s im ilar to the qu estion, analyze carefully whether they are truly relevant or only superficially similar but unrelated

    Extr act inf or m ation f r om the r etr ieved docu m ents th at m ay be r elevant to the m ain qu estion . Fo r statem ents s im ilar to the qu estion, analyze carefully whether they are truly relevant or only superficially similar but unrelated. If no relevant information is present, skip step 5 and ass ign a retriev al_ rewardof 0

  12. [12]

    an aly sis

    For each r elevant inf or m ation, check wh ether it was alr eady r etr ieved in p r eviou s r ou nd s an d, if so, in wh ich r oun d. Finally,giv e the retriev al_ rewardbased on whether genuinely new relevant information was retrieved in this turn. Please conduct your analys is in the order above and justify your scoring. ### For m at In p u t fo rmat: ...

    2018