# Experiential Reflective Learning for Self-Improving LLM Agents Source: [Experiential Reflective Learning for Self-Improving LLM Agents]() Collection index: [llms.txt]() Data updated: 2026-04-03T05:00:04.568Z Authors: Marc-Antoine Allard, Arnaud Teinturier, Victor Xing, Gautier Viaud Published: 2026-03-25T11:43:22.000Z ## Abstract Recent advances in large language models (LLMs) have enabled the development of autonomous agents capable of complex reasoning and multi-step problem solving. However, these agents struggle to adapt to specialized environments and do not leverage past interactions, approaching each new task from scratch regardless of their accumulated experience. We introduce Experiential Reflective Learning (ERL), a simple self-improvement framework that enables rapid environment adaptation through experiential learning. ERL reflects on task trajectories and outcomes to generate heuristics, capturing actionable lessons that transfer across tasks. At test time, relevant heuristics are retrieved based on the current task and injected into the agent's context to guide execution. On the Gaia2 benchmark, ERL improves success rate by 7.8% over a ReAct baseline, with large gains in task completion reliability, and outperforms prior experiential learning methods. Through systematic ablations, we find that selective retrieval is essential and that heuristics provide more transferable abstractions than few-shot trajectory prompting. These results demonstrate that reflecting on single-attempt experiences to extract transferable heuristics enables effective agent self-improvement. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Task Resolution on τ2-bench (test)](): Task Resolution; τ2-BENCH (test); Success Rate (Airline): 38.3. ## Original sources and implementation [Original paper]() [PDF]()