Internalizing Agency from Reflective Experience

• URL: http://arxiv.org/abs/2603.16843v1
• Date: Tue, 17 Mar 2026 17:50:47 GMT
• Title: Internalizing Agency from Reflective Experience
• Abstract summary: LEAFE is a framework that internalizes recovery agency from reflective experience.<n>It consistently improves Pass@1 over the base model and achieves higher Pass@k than outcome-driven baselines.
• Score: 20.650609947690196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models are increasingly deployed as autonomous agents that must plan, act, and recover from mistakes through long-horizon interaction with environments that provide rich feedback. However, prevailing outcome-driven post-training methods (e.g., RL with verifiable rewards) primarily optimize final success signals, leaving rich environment feedback underutilized. Consequently, they often lead to distribution sharpening: the policy becomes better at reproducing a narrow set of already-successful behaviors, while failing to improve the feedback-grounded agency needed to expand problem-solving capacity (e.g., Pass@k) in long-horizon settings. To address this, we propose LEAFE (Learning Feedback-Grounded Agency from Reflective Experience), a framework that internalizes recovery agency from reflective experience. Specifically, during exploration, the agent summarizes environment feedback into actionable experience, backtracks to earlier decision points, and explores alternative branches with revised actions. We then distill these experience-guided corrections into the model through supervised fine-tuning, enabling the policy to recover more effectively in future interactions. Across a diverse set of interactive coding and agentic tasks under fixed interaction budgets, LEAFE consistently improves Pass@1 over the base model and achieves higher Pass@k than outcome-driven baselines (GRPO) and experience-based methods such as Early Experience, with gains of up to 14% on Pass@128.

Related papers

• CAFE: Self-Improving Search Agents Need Co-Evolving Feedback [64.5257526832476]
  Outcome-supervised search agents learn when and how to retrieve evidence, but terminal rewards neither localize intermediate errors nor redirect an ongoing trajectory before those errors compound.<n>Treating corrective feedback as a learned in-trajectory intervention couples the two roles: the agent must decide when to request and use feedback, while the critic must infer useful corrections from outcome-confounded rollouts whose failure patterns shift as the agent improves.
  arXiv  Detail & Related papers  (2026-08-25T16:39:00Z)
• EDGE: Experience-Distillation for Guided Exploration in Agentic Reinforcement Learning [25.200068256200293]
  reusable exploration patterns embedded in interaction trajectories are largely discarded after a single policy update.<n>We propose EDGE (Experience-Distillation for Guided Exploration), a framework that treats retrieved experiences as temporary training-time scaffolds.<n> EDGE improves over strong RL baselines by up to 12.5 points and remains effective without inference-time scaffolds or a proprietary reflector.
  arXiv  Detail & Related papers  (2026-08-22T13:16:25Z)
• PATH-Bench: Path-Dependent Evaluation of Lifelong Agents [27.837334107109786]
  PATH-Bench is a benchmark for path-dependent evaluation of lifelong agents.<n>We propose Selective Experience Use (SEU), an agent harness that regulates how path-accumulated experience influences each new task.
  arXiv  Detail & Related papers  (2026-08-02T11:00:04Z)
• SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning [38.54413500261524]
  Outcome-based reinforcement learning (RL) provides a practical optimization paradigm, but its sparse trajectory-level rewards offer limited guidance on intermediate decisions.<n>We propose SEED (SElf-Evolving On-Policy Distillation), a self-evolving framework that converts completed on-policy trajectories into training-time hindsight skills.
  arXiv  Detail & Related papers  (2026-07-16T09:57:18Z)
• HERO: Hindsight-Enhanced Reflection from Environment Observations for Agentic Self-Distillation [50.53459634301361]
  HERO is a hindsight-enhanced self-distillation framework that uses next environment observations as locally aligned feedback.<n> HERO improves task success and reduces unnecessary turns over environment-feedback-only self-distillation and GRPO.
  arXiv  Detail & Related papers  (2026-06-10T01:35:34Z)
• Experiential Reflective Learning for Self-Improving LLM Agents [1.1074589887824053]
  We introduce Experiential Reflective Learning (ERL), a simple self-improvement framework that enables rapid environment adaptation.<n>ERL reflects on task trajectories and outcomes to generate actionable lessons that transfer across tasks.<n>ERL improves success rate by 7.8% over a ReAct baseline, with large gains in task completion reliability.
  arXiv  Detail & Related papers  (2026-03-25T11:43:22Z)
• RetroAgent: From Solving to Evolving via Retrospective Dual Intrinsic Feedback [54.39884046754265]
  RetroAgent is an online RL framework that enables agents to master complex interactive environments.<n>Our experiments show that RetroAgent achieves state-of-the-art (SOTA) performance.
  arXiv  Detail & Related papers  (2026-03-09T16:23:33Z)
• Agent Learning via Early Experience [93.83579011718858]
  A long-term goal of language agents is to learn and improve through their own experience, ultimately outperforming humans in complex, real-world tasks.<n>Most current agents rely on supervised fine-tuning on expert data, which is challenging to scale and generalizes poorly.<n>We study two strategies of using such data: (1) Implicit world modeling, which uses collected states to ground the policy in environment dynamics; and (2) Self-reflection, where the agent learns from its suboptimal actions to improve reasoning and decision-making.
  arXiv  Detail & Related papers  (2025-10-09T17:59:17Z)
• From Novice to Expert: LLM Agent Policy Optimization via Step-wise Reinforcement Learning [62.54484062185869]
  We introduce StepAgent, which utilizes step-wise reward to optimize the agent's reinforcement learning process.<n>We propose implicit-reward and inverse reinforcement learning techniques to facilitate agent reflection and policy adjustment.
  arXiv  Detail & Related papers  (2024-11-06T10:35:11Z)
• Iterative Experience Refinement of Software-Developing Agents [81.09737243969758]
  Large language models (LLMs) can leverage past experiences to reduce errors and enhance efficiency. This paper introduces the Iterative Experience Refinement framework, enabling LLM agents to refine experiences iteratively during task execution.
  arXiv  Detail & Related papers  (2024-05-07T11:33:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.