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

TL;DR

SEARL introduces a structured experience memory framework for self-evolving agents, enhancing learning efficiency and generalization in resource-limited environments by integrating planning, execution, and explicit knowledge extraction.

Contribution

The paper presents a novel Tool-Memory based framework that constructs structured experience memory for improved self-evolving agent learning, addressing reward sparsity and resource constraints.

Findings

Effective in knowledge reasoning tasks

Improves learning efficiency in mathematics tasks

Facilitates tool reuse and generalization

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.