Experience-driven self-evolution is critical for large language model (LLM) agents to improve through open-world interaction. However, existing experience learning methods mostly rely on single-agent loops, where the same agent executes tasks, summarizes outcomes, and determines memory content.
The paper introduces Self‑Direction, a method that lets agents learn from mistakes by reflecting on failed trajectories, distilling lessons into lightweight low‑rank adapters, and storing them for future use. These adapters add only about 0.02% of the base model’s parameters, keeping the original weights frozen and avoiding costly fine‑tuning. Experiments on Qwen‑based agents across research, tool‑use, and terminal tasks show 8.9–17.0 percentage‑point gains in success rates, reduced repeated errors, and transfer of lessons to new tasks.
By Yuval Kansal, Karen Ullrich, Polina Kirichenko, Ilia Kulikov, Niraj K. Jha, Mohammad Pezeshki, Mark Ibrahim
The paper explores Retrospection-Only Fine-Tuning (ROFT), a method where a language-model agent improves its behavior by generating and training on explanations of its own experiences, without external teachers or reward signals. In software‑engineering tasks with Qwen3.5‑4B, ROFT achieves comparable or better solve rates than GRPO while requiring fewer updates and training time, and can learn from failures alone. Behavioral analysis shows ROFT indirectly assigns credit to actions and can produce shorter, more direct solutions when prompted to focus on direct solutions.
The paper introduces On-Policy Reminder Distillation (OPRD), a method that enhances long-horizon coding agents by combining multi-scope reminder supervision with outcome-based routing. OPRD uses successful rollouts as autoregressive self-imitation targets and failed rollouts for reminder-conditioned reverse-KL supervision from a teacher that re-evaluates student prefixes. The approach improves task resolution rates by 11.3 points and cuts error events by 50.2% on several SWE-bench benchmarks.
By Yuechen Luo, Zhiyi Lai, Miao Ziqi, Zihang Xu, Jingxuan Xu, Songwei Yu, Wenqiang Zhu, Weihao Li, Junhao Fan, Haoyang Huang, Yanan Wu, Ken Deng
AHEAD is a step‑aware framework that augments reinforcement learning for multi‑turn LLM agents by matching different supervision sources to different step types. The teacher receives environment feedback on all steps and LLM‑generated corrective hints only on error steps, providing finer‑grained guidance than uniform trajectory‑level rewards. Across ALFWorld, WebShop, and Search‑based QA, AHEAD improves task success by 13.3 points on ALFWorld and 11.0 on WebShop at 7B, reaches target success rates faster, and solves tasks within tighter interaction budgets compared to outcome‑only RL and prior self‑distillation baselines.
By Xiaolong Jin, Dingmin Wang, Vijay Lingam, Varun Kumar
The paper introduces Boundary-Aware Skill Memory (BASM), a method that enriches skill memories for large language model agents with explicit boundary fields such as applicability conditions, risk cues, avoidance rules, and recovery notes. This approach transforms retrieved skills from unconditional templates into state‑conditioned guidance, preventing the Skill Imitation Trap where more skills lead to incorrect tool usage. Experiments on three agent benchmarks and four model scales show that BASM improves task success rates, accuracy, and reduces attack success while cutting average steps compared to memory‑free baselines.
By Zihan Lin, Zhenyu Chen, Jiawen Wei, Xiaohan Wang, Jie Cao, Jiajun Chai, Wei Lin, Guojun Yin, Ran He