SAMULE: Self-Learning Agents Enhanced by Multi-level Reflection

• URL: http://arxiv.org/abs/2509.20562v1
• Date: Wed, 24 Sep 2025 21:02:15 GMT
• Title: SAMULE: Self-Learning Agents Enhanced by Multi-level Reflection
• Authors: Yubin Ge, Salvatore Romeo, Jason Cai, Monica Sunkara, Yi Zhang,
• Abstract summary: SAMULE is a new framework for self-learning agents powered by a retrospective language model that is trained based on Multi-Level Reflection Synthesis.<n>It first synthesizes high-quality reflections across three complementary levels: Single-Trajectory Learning (micro-level) for detailed error correction; Intra-Task Learning (meso-level) to build error across multiple trials of the same task, and Inter-Task Learning (macro-level) to extract transferable insights based on same typed errors from diverse task failures.
• Score: 14.40651157974557
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the rapid advancements in LLM agents, they still face the challenge of generating meaningful reflections due to inadequate error analysis and a reliance on rare successful trajectories, especially in complex tasks. In this work, we propose SAMULE, a new framework for self-learning agents powered by a retrospective language model that is trained based on Multi-Level Reflection Synthesis. It first synthesizes high-quality reflections across three complementary levels: Single-Trajectory Learning (micro-level) for detailed error correction; Intra-Task Learning (meso-level) to build error taxonomies across multiple trials of the same task, and Inter-Task Learning (macro-level) to extract transferable insights based on same typed errors from diverse task failures. Then we fine-tune a language model serving as the retrospective model to generate reflections during inference. We further extend our framework to interactive settings through a foresight-based reflection mechanism, enabling agents to proactively reflect and adapt during user interactions by comparing predicted and actual responses. Extensive experiments on three challenging benchmarks - TravelPlanner, NATURAL PLAN, and Tau-bench - demonstrate that our approach significantly outperforms reflection-based baselines. Our results highlight the critical role of well-designed reflection synthesis and failure-centric learning in building self-improving LLM agents.

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