arxiv: 2604.07791 · v3 · submitted 2026-04-09 · 💻 cs.AI · cs.LG

Recognition: unknown

SEARL: Joint Optimization of Policy and Tool Graph Memory for Self-Evolving Agents

Xinshun Feng , Xinhao Song , Lijun Li , Gongshen Liu , Jing Shao

Authors on Pith no claims yet

Pith reviewed 2026-05-10 17:22 UTC · model grok-4.3

classification 💻 cs.AI cs.LG

keywords self-evolving agentstool graph memoryreinforcement learning with verifiable rewardsagentic learningstate abstractionsparse rewardspolicy optimizationtrajectory correlation

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The pith

SEARL constructs a tool graph memory from trajectories that integrates planning and execution to generalize tool reuse and densify sparse rewards.

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

The paper presents SEARL as a self-evolving agent framework that builds a structured experience memory instead of relying on raw interaction histories. This memory combines planning steps with execution outcomes to create a new way of representing states that lets the agent recognize and reuse similar tools across different tasks. By pulling explicit patterns out of past data and using links between separate trajectories, the approach turns infrequent final rewards into more frequent learning signals. The method targets resource-limited settings where large language models or separate agent teams are not feasible, and it reports results on knowledge reasoning and mathematics benchmarks.

Core claim

SEARL jointly optimizes the agent's policy together with a tool graph memory built from interaction trajectories. The memory supplies a novel state abstraction that supports generalization across analogous contexts, such as reusing the same tool in new situations, while inter-trajectory correlations convert sparse outcome rewards into denser training signals, allowing agents to extract explicit knowledge from history without depending on large-scale LLMs or multi-agent systems.

What carries the argument

Tool graph memory: a structured representation of tools and their relational usage patterns extracted from trajectories, jointly optimized with the policy to link planning and execution for abstraction and reward densification.

If this is right

Agents extract explicit reusable knowledge from historical trajectories rather than treating each interaction in isolation.
Inter-trajectory correlations provide additional reward signals that accelerate learning under outcome-only feedback.
The framework achieves effective self-evolution on knowledge reasoning and mathematics tasks in resource-constrained environments.
Joint policy and memory optimization enables tool reuse across analogous contexts without external scaffolding.

Where Pith is reading between the lines

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

Smaller base models could handle more complex multi-step agent tasks if the memory structure compensates for limited reasoning capacity.
The same graph construction might extend to domains like code generation or web navigation where repeated tool patterns appear across episodes.
If the graph encodes causal dependencies between tools, it could improve robustness when environment dynamics change.

Load-bearing premise

That building a tool graph memory from past interaction trajectories will reliably turn sparse final rewards into denser learning signals and support generalization to similar tasks without needing larger models or multiple agents.

What would settle it

A direct comparison on the same reasoning or math benchmarks showing that SEARL produces no measurable gain in task success rate or sample efficiency over standard RLVR baselines when using identical base model sizes and single-agent setups.

Figures

Figures reproduced from arXiv: 2604.07791 by Gongshen Liu, Jing Shao, Lijun Li, Xinhao Song, Xinshun Feng.

**Figure 2.** Figure 2: Overview of the proposed framework. The architecture consists of three components: (1) Agent [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 4.** Figure 4: Ablation study illustrating the impact of re [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 3.** Figure 3: Comparison of training rewards and entropy [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 5.** Figure 5: Comparison on constrained geometric sequence optimization. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Structural Growth of Tool Subgraphs from [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗

read the original abstract

Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) have demonstrated significant potential in single-turn reasoning tasks. With the paradigm shift toward self-evolving agentic learning, models are increasingly expected to learn from trajectories by synthesizing tools or accumulating explicit experiences. However, prevailing methods typically rely on large-scale LLMs or multi-agent frameworks, which hinder their deployment in resource-constrained environments. The inherent sparsity of outcome-based rewards also poses a substantial challenge, as agents typically receive feedback only upon completion of tasks. To address these limitations, we introduce a Tool-Memory based self-evolving agentic framework SEARL. Unlike approaches that directly utilize interaction experiences, our method constructs a structured experience memory that integrates planning with execution. This provides a novel state abstraction that facilitates generalization across analogous contexts, such as tool reuse. Consequently, agents extract explicit knowledge from historical data while leveraging inter-trajectory correlations to densify reward signals. We evaluate our framework on knowledge reasoning and mathematics tasks, demonstrating its effectiveness in achieving more practical and efficient learning.

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

SEARL builds a tool-graph memory from trajectories to densify rewards and aid generalization in small-scale self-evolving agents, but the abstract gives no numbers or baselines to show it works.

read the letter

The main thing to know about this paper is that it proposes SEARL as a way to make self-evolving agents practical in resource-limited settings. It does this by turning interaction trajectories into a structured tool graph memory that mixes planning and execution, supposedly letting agents reuse tools across similar tasks and pull denser signals from correlations between different runs instead of waiting for final outcomes.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces SEARL, a Tool-Memory based self-evolving agentic framework for RLVR. It constructs a structured experience memory from interaction trajectories that integrates planning with execution, providing a novel state abstraction to facilitate generalization across analogous contexts such as tool reuse. Agents are said to extract explicit knowledge from historical data while leveraging inter-trajectory correlations to densify sparse outcome-based rewards. The framework is evaluated on knowledge reasoning and mathematics tasks and is positioned as enabling effective self-evolving agents without large-scale LLMs or multi-agent systems.

Significance. If the empirical claims hold, the work could provide a practical path toward efficient self-evolving agents in resource-constrained settings by using tool-graph memory for state abstraction and reward densification. The joint optimization of policy and memory is a potentially useful direction for agentic learning that avoids heavy reliance on external frameworks.

major comments (2)

[Abstract/Evaluation] Abstract and evaluation description: the paper asserts effectiveness on reasoning and mathematics tasks but supplies no metrics, baselines, ablation studies, or experimental setup details to substantiate the central claims of reward densification and generalization via the tool graph memory.
[Method] Method section: the construction of the tool graph memory, the integration of planning and execution, and the mechanism for leveraging inter-trajectory correlations are described at a high level without equations, algorithms, or formal definitions, leaving the novelty of the state abstraction and its claimed benefits without mathematical grounding or reproducibility details.

minor comments (2)

[Abstract] The abstract would be strengthened by including at least one key quantitative result or comparison to convey the scale of improvement.
[Method] Consider adding a diagram or pseudocode for the tool graph memory update and joint optimization procedure to improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive review. We address each major comment below and will incorporate revisions to strengthen the manuscript's clarity, formality, and completeness.

read point-by-point responses

Referee: [Abstract/Evaluation] Abstract and evaluation description: the paper asserts effectiveness on reasoning and mathematics tasks but supplies no metrics, baselines, ablation studies, or experimental setup details to substantiate the central claims of reward densification and generalization via the tool graph memory.

Authors: We acknowledge that the abstract provides only a high-level summary of the evaluation without quantitative metrics or setup details. The full manuscript includes experimental results on knowledge reasoning and mathematics tasks demonstrating effectiveness, but to directly address this concern we will revise the abstract to include key performance indicators (e.g., success rate improvements) and expand the evaluation section with explicit baselines, ablation studies (such as variants without the tool graph memory), experimental hyperparameters, and quantitative evidence supporting reward densification via inter-trajectory correlations and generalization through state abstraction. revision: yes
Referee: [Method] Method section: the construction of the tool graph memory, the integration of planning and execution, and the mechanism for leveraging inter-trajectory correlations are described at a high level without equations, algorithms, or formal definitions, leaving the novelty of the state abstraction and its claimed benefits without mathematical grounding or reproducibility details.

Authors: We agree that the method description would benefit from greater formality and detail. In the revised manuscript we will introduce formal definitions and equations for the tool graph memory construction, the state abstraction mechanism, and the reward densification process based on inter-trajectory correlations. We will also add an algorithm box outlining the joint optimization of policy and memory, along with explicit steps for integrating planning and execution phases, to provide mathematical grounding and ensure reproducibility. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The provided abstract and description contain no equations, derivations, fitted parameters, or predictions. The framework is introduced descriptively as a novel state abstraction via tool graph memory, with claims about generalization and reward densification, but these are not shown to reduce to inputs by construction. No self-citations, ansatzes, or uniqueness theorems are invoked in the text. The derivation chain is absent, making the paper self-contained at the descriptive level with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The central claim rests on the unproven premise that structured memory from trajectories can densify rewards and generalize without large models; no free parameters or invented entities are quantified in the abstract.

axioms (2)

domain assumption Agents receive only sparse outcome-based rewards upon task completion
Stated as inherent challenge in prevailing methods
ad hoc to paper Structured experience memory enables better state abstraction and inter-trajectory correlations
Core assumption of the SEARL framework

invented entities (1)

Tool Graph Memory no independent evidence
purpose: Structured memory integrating planning and execution for generalization and reward densification
Newly introduced component central to the framework

pith-pipeline@v0.9.0 · 5487 in / 1266 out tokens · 49224 ms · 2026-05-10T17:22:58.905857+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

5 extracted references · 4 canonical work pages · 2 internal anchors

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Let’s verify step by step. InThe Twelfth Inter- national Conference on Learning Representations. Jiaye Lin, Yifu Guo, Yuzhen Han, Sen Hu, Ziyi Ni, Licheng Wang, Mingguang Chen, Daxin Jiang, Binx- ing Jiao, Chen Hu, and 1 others. 2025. Se-agent: Self-evolution trajectory optimization in multi-step reasoning with llm-based agents.arXiv preprint arXiv:2508.0...

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WebArena: A Realistic Web Environment for Building Autonomous Agents

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