arxiv: 2602.02556 · v2 · submitted 2026-01-30 · 💻 cs.LG · cs.AI

Recognition: no theorem link

Beyond Experience Retrieval: Learning to Generate Utility-Optimized Structured Experience for Frozen LLMs

Xuancheng Li , Haitao Li , Yujia Zhou , Yiqun Liu , Qingyao Ai

Authors on Pith no claims yet

Pith reviewed 2026-05-16 09:40 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords structured experienceadapter modulefrozen LLMsutility optimizationGRPOmathematical reasoningexperience generationplug-in module

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The pith

SEAM learns to generate structured experience entries optimized for utility to guide frozen LLMs without retrieval or weight changes.

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

The paper proposes replacing similarity-based experience retrieval with a learned generator that produces tailored, structured experience for each new problem. SEAM trains this generator inside a small adapter module using rollout outcomes from the frozen LLM executor and GRPO, so the main model stays unchanged. If successful, the approach lets users improve reasoning performance through a lightweight plug-in that adds little latency and can be refined later on logged successes. A reader would care because it addresses the static nature of deployed LLMs by turning past interactions into reusable guidance stored in parameters rather than an external database.

Core claim

SEAM is a lightweight, executor-specific plug-in whose parameters store experience and generate a structured, instance-tailored experience entry in one forward pass; it is trained for utility via executor rollouts and GRPO while the LLM executor remains frozen, and it can be further improved after deployment with supervised fine-tuning on successful trajectories, producing consistent accuracy gains on mathematical reasoning benchmarks at low overhead.

What carries the argument

The Structured Experience Adapter Module (SEAM), which stores experience in its own parameters and produces instance-specific structured guidance entries in a single forward pass to steer the frozen executor.

If this is right

Accuracy rises consistently across multiple frozen LLM executors on mathematical reasoning benchmarks.
Generation adds only the cost of one forward pass through the small adapter instead of retrieval latency.
The adapter can be updated post-deployment by supervised fine-tuning on logged successful trajectories.
Experience becomes executor-specific and avoids noise from similarity-based external retrieval.

Where Pith is reading between the lines

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

Similar adapters could be trained for domains beyond math such as code generation or multi-step planning.
SEAM might be combined with retrieval systems to produce hybrid guidance that mixes generated and retrieved entries.
The method implies that utility-based training of small adapters can make static models more reliable across repeated uses without touching base-model weights.

Load-bearing premise

That experiences generated by SEAM via GRPO rollouts on frozen executors provide genuine utility gains that generalize and do not introduce new failure modes.

What would settle it

A side-by-side evaluation on held-out mathematical reasoning problems in which attaching SEAM produces no accuracy increase or produces a decrease relative to the frozen LLM alone.

Figures

Figures reproduced from arXiv: 2602.02556 by Haitao Li, Qingyao Ai, Xuancheng Li, Yiqun Liu, Yujia Zhou.

**Figure 2.** Figure 2: The cross-domain results for SEAM with Qwen3-4B [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

**Figure 3.** Figure 3: The results of scaling with experience size. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

**Figure 4.** Figure 4: Input prompt for SEAM. from C to AB. • Use the triangle area formula: since ABC is a right triangle, its area is 1 2 × AC × BC = 1 2 × 8 × 15 = 60. • Find the area of triangle ACD using coordinates: with A, C, and D, apply the shoelace formula or the “base × height / 2” formula. • Similar triangles or trigonometry could also be used, but in this case, coordinate geometry is more efficient. </experience> <e… view at source ↗

**Figure 5.** Figure 5: Input prompts for different executors. h = (8 × 15)/17 = 120/17. But wait, that’s the length of the altitude from C to AB, which is CD. But the question asks for the area of triangle ACD. Since D is the foot of the altitude, CD is the height of triangle ACD with base AD. So I need to find AD. In a right triangle, the length of the segments into which the altitude divides the hypotenuse can be found using s… view at source ↗

read the original abstract

Large language models (LLMs) are largely static and often redo reasoning or repeat mistakes. Prior experience reuse typically relies on external retrieval, which is similarity-based, can introduce noise, and adds latency. We introduce SEAM (Structured Experience Adapter Module), a lightweight, executor-specific plug-in that stores experience in its parameters and generates a structured, instance-tailored experience entry in a single forward pass to guide a frozen LLM executor. SEAM is trained for utility via executor rollouts and GRPO while keeping the executor frozen, and it can be further improved after deployment with supervised fine-tuning on logged successful trajectories. Experiments on mathematical reasoning benchmarks show consistent accuracy gains across executors with low overhead. Extensive ablations and analyses further elucidate the mechanisms underlying SEAM's effectiveness and robustness.

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.

Desk Editor's Note private letter to a colleague

SEAM introduces a trainable adapter that generates utility-optimized structured experiences in one pass for frozen LLMs via GRPO rollouts, offering a retrieval-free alternative, though gains may not generalize if training and test distributions overlap.

read the letter

The core of this paper is SEAM, a lightweight adapter that learns to output a structured experience entry tailored to the current query in a single forward pass. It gets trained by running the frozen executor on rollouts and using GRPO to optimize for actual task utility rather than similarity scores. The executor stays untouched, which keeps the approach modular across different models, and the abstract notes it can be refined later with logged successes. This setup directly targets the latency and noise problems of pulling past examples from a store. Experiments on math reasoning benchmarks report consistent accuracy improvements with low overhead, backed by ablations that probe the mechanisms. That part is a clean shift from prior retrieval-heavy reuse methods. The main soft spot is generalization. The GRPO training relies on executor rollouts whose utility is measured on the same class of mathematical reasoning problems used for final evaluation. Without an explicit statement that the training problems are strictly disjoint from the test sets in distribution or template, the gains could partly reflect matching the benchmark style instead of producing broadly useful experiences. The abstract does not address this split, so the results need verification for whether new failure modes appear on truly unseen problems. This work is aimed at groups working on LLM reasoning and adaptation who want to boost performance without full fine-tuning or external retrieval systems. A reader focused on efficient plug-in methods would find the single-pass generation and utility-based training worth examining. It deserves peer review because the proposal is distinct and the training setup is reproducible in principle, even if the experiments require close checking on distribution and robustness.

Referee Report

2 major / 3 minor

Summary. The manuscript introduces SEAM (Structured Experience Adapter Module), a lightweight executor-specific plug-in that stores experience in its parameters and generates structured, instance-tailored experience entries in a single forward pass to guide a frozen LLM executor. SEAM is trained for utility optimization via GRPO on executor rollouts (with the executor kept frozen) and can be further refined post-deployment via supervised fine-tuning on logged successful trajectories. Experiments on mathematical reasoning benchmarks report consistent accuracy gains across executors with low overhead, supported by ablations and analyses of underlying mechanisms.

Significance. If the results hold under proper controls, the work offers a practical alternative to retrieval-based experience reuse by directly learning to generate utility-optimized structured experience. The frozen-executor constraint, low overhead, and post-deployment adaptability are strengths that could enable broader deployment on reasoning tasks without retraining large models. The use of GRPO on external rollouts and the reported ablations provide concrete evidence of effectiveness and robustness when the training distribution is appropriately separated from evaluation.

major comments (2)

[§4 (Experimental Setup)] §4 (Experimental Setup): the manuscript does not explicitly confirm that the problems used for GRPO training rollouts are strictly disjoint from the test instances in the mathematical reasoning benchmarks. Because utility is measured directly on these benchmarks, any distributional overlap (e.g., shared templates or difficulty bands) would undermine the central claim that SEAM produces generalizable utility gains rather than benchmark-specific overfitting.
[§5 (Results)] §5 (Results): while 'consistent accuracy gains' are asserted, the reported tables lack per-benchmark deltas, number of runs, standard deviations, or statistical significance tests. Without these, it is impossible to judge whether the improvements are reliable or merely within noise, which is load-bearing for the claim of consistent gains across executors.

minor comments (3)

[§3.2 (GRPO Objective)] §3.2 (GRPO Objective): the definition of the utility reward used in the GRPO objective should be stated explicitly (including any normalization or executor-specific scaling) rather than left implicit from the rollout description.
[Figure 3] Figure 3 (Ablation plots): axis labels and legend entries are too small for readability; increasing font size and adding a short caption summarizing the key takeaway would improve clarity.
[§2 (Related Work)] §2 (Related Work): the comparison to retrieval methods would benefit from a brief quantitative reference to typical latency overheads reported in the cited retrieval papers, to better contextualize SEAM's claimed low-overhead advantage.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and have revised the paper accordingly to improve clarity and rigor.

read point-by-point responses

Referee: [§4 (Experimental Setup)] §4 (Experimental Setup): the manuscript does not explicitly confirm that the problems used for GRPO training rollouts are strictly disjoint from the test instances in the mathematical reasoning benchmarks. Because utility is measured directly on these benchmarks, any distributional overlap (e.g., shared templates or difficulty bands) would undermine the central claim that SEAM produces generalizable utility gains rather than benchmark-specific overfitting.

Authors: We confirm that the GRPO training rollouts were generated exclusively from the training splits of each benchmark (e.g., GSM8K train, MATH train), which are strictly disjoint from the held-out test sets used for evaluation. No test instances or templates were used during training or rollout generation. We will add an explicit statement and table of data splits in the revised §4 to document this separation and reinforce the generalizability claim. revision: yes
Referee: [§5 (Results)] §5 (Results): while 'consistent accuracy gains' are asserted, the reported tables lack per-benchmark deltas, number of runs, standard deviations, or statistical significance tests. Without these, it is impossible to judge whether the improvements are reliable or merely within noise, which is load-bearing for the claim of consistent gains across executors.

Authors: We agree that these statistical details are necessary. In the revised §5, we will expand the tables to report per-benchmark accuracy deltas, results averaged over 5 independent runs with standard deviations, and paired t-test p-values to establish statistical significance of the gains. These additions will directly support the reliability of the reported improvements. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain

full rationale

The paper's core method trains SEAM via GRPO on executor rollouts from a frozen LLM, then evaluates accuracy gains on mathematical reasoning benchmarks. This setup relies on external rollouts for utility optimization rather than any self-defined quantities or fitted parameters renamed as predictions. No self-definitional loops, load-bearing self-citations, uniqueness theorems imported from prior author work, or ansatz smuggling appear in the described chain. The derivation remains self-contained with independent content from the rollout-based training process.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the domain assumption that utility can be directly optimized through rollout-based training of an adapter without access to full model parameters or external retrieval.

axioms (1)

domain assumption Executor rollouts provide reliable utility signals for training the experience generator
Invoked in the description of SEAM training via GRPO while keeping executor frozen.

invented entities (1)

SEAM (Structured Experience Adapter Module) no independent evidence
purpose: Stores experience in parameters and generates instance-tailored structured entries in one forward pass
New module introduced to replace retrieval-based experience reuse

pith-pipeline@v0.9.0 · 5443 in / 1198 out tokens · 22406 ms · 2026-05-16T09:40:59.496653+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

13 extracted references · 13 canonical work pages · 3 internal anchors

[1]

Training Verifiers to Solve Math Word Problems

Training verifiers to solve math word prob- lems.arXiv preprint arXiv:2110.14168. Guanting Dong, Yifei Chen, Xiaoxi Li, Jiajie Jin, Hongjin Qian, Yutao Zhu, Hangyu Mao, Guorui Zhou, Zhicheng Dou, and Ji-Rong Wen. 2025. Tool-star: Empowering llm-brained multi-tool rea- soner via reinforcement learning.arXiv preprint arXiv:2505.16410. Yiming Du, Wenyu Huang...

work page internal anchor Pith review Pith/arXiv arXiv 2025
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Meminsight: Autonomous memory augmenta- tion for llm agents.arXiv preprint arXiv:2503.21760. Zhengliang Shi, Yiqun Chen, Haitao Li, Weiwei Sun, Shiyu Ni, Yougang Lyu, Run-Ze Fan, Bowen Jin, Yixuan Weng, Minjun Zhu, Qiujie Xie, Xinyu Guo, Qu Yang, Jiayi Wu, Jujia Zhao, Xiaqiang Tang, Xin- bei Ma, Cunxiang Wang, Jiaxin Mao, and 7 others

work page arXiv
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Mirac Suzgun, Mert Yuksekgonul, Federico Bianchi, Dan Jurafsky, and James Zou

Deep research: A systematic survey.Preprint, arXiv:2512.02038. Mirac Suzgun, Mert Yuksekgonul, Federico Bianchi, Dan Jurafsky, and James Zou. 2025. Dynamic cheat- sheet: Test-time learning with adaptive memory. arXiv preprint arXiv:2504.07952. Xiaoyu Tan, Bin Li, Xihe Qiu, Chao Qu, Wei Chu, Yinghui Xu, and Yuan Qi. 2025. Meta-agent- workflow: Streamlining...

work page arXiv 2025
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RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning

Ragen: Understanding self-evolution in llm agents via multi-turn reinforcement learning.arXiv preprint arXiv:2504.20073. Ziting Wang, Haitao Yuan, Wei Dong, Gao Cong, and Feifei Li. 2024a. Corag: A cost-constrained retrieval optimization system for retrieval-augmented genera- tion.arXiv preprint arXiv:2411.00744. Zora Zhiruo Wang, Jiayuan Mao, Daniel Frie...

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Qwen3 Technical Report

Qwen3 technical report.arXiv preprint arXiv:2505.09388. Yijun Yang, Tianyi Zhou, Kanxue Li, Dapeng Tao, Lu- song Li, Li Shen, Xiaodong He, Jing Jiang, and Yuhui Shi. 2024. Embodied multi-modal agent trained by an llm from a parallel textworld. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 26275–26285. 11 Zhilin...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[6]

Keep it short

<analysis> – a brief, executor-aware diagnosis of what the problem is asking and what is likely to be tricky (e.g., key constraints, typical failure modes, and the most suitable strategy family). Keep it short

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[7]

Draw on the executor’s typical strengths and weaknesses to give algorithms, heuristics, and pitfalls that will most help it on future attempts

<experience> – a bullet list of distilled tips designed for the downstream executor that will help it solve the current problem <problem> {problem} </problem> and similar tasks in the future. Draw on the executor’s typical strengths and weaknesses to give algorithms, heuristics, and pitfalls that will most help it on future attempts

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[8]

— ## Output Format <analysis>

<example> – 3–8 concise, high-level steps you judge to be the most effective solution path for the executor on the problem <problem> {problem} </problem>. — ## Output Format <analysis> . . . brief diagnosis and strategy </analysis> <experience> •. . . first distilled insight •. . . second distilled insight •. . . etc. </experience> <example>

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[9]

base×height / 2

</example> Figure 4: Input prompt for SEAM. fromCtoAB. •Use the triangle area formula: since ABCis a right triangle, its area is 1 2 ×AC×BC= 1 2 ×8×15 = 60. •Find the area of triangleACDusing coordinates: withA,C, andD, apply the shoelace formula or the “base×height / 2” formula. •Similar triangles or trigonometry could also be used, but in this case, coo...

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[10]

Step 1 — Use the Pythagorean theorem to determine whether triangleABCis right-angled

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[11]

Step 2 — Place the triangle on a coordinate plane withCat(0,0),Aat (0,8), andBat(15,0)

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[12]

Step 3 — Find the coordinates ofD, the foot of the altitude fromCtoAB

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[13]

</example> Executor Output <think> We are asked to find the area of triangle ACDinside triangleABC, whereAB= 17, AC= 8,BC= 15, andDis the foot of the altitude fromCtoAB

Step 4 — Using the coordinates ofA,C, andD, compute the area of triangleACD with the shoelace formula. </example> Executor Output <think> We are asked to find the area of triangle ACDinside triangleABC, whereAB= 17, AC= 8,BC= 15, andDis the foot of the altitude fromCtoAB. First, the user mentioned that the triangle is a right triangle. Let me confirm that...

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