More Methods, Better Agents? A Reality Check on Self-Distillation in Agentic Reinforcement Learning
cs.CL, cs.LG
Submitted: 2026-10-14
Updated: 2026-10-14
Code: https://github.com/aim-uofa/Self-Distillation-AgentRL
Project page: https://aim-uofa.github.io/Self-Distillation-AgentRL
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent
- FireAct: Toward Language Agent Fine-tuning
- Privileged, but Biased: How PI-Conditioned Teachers Break Self-Distillation
- Distilling the Knowledge in a Neural Network
- Unifying distillation and privileged information
- Self-Distilled Agentic Reinforcement Learning
- PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning
- Policy Distillation
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
- Skill-SD: Skill-Conditioned Self-Distillation for Multi-turn LLM Agents
- AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning
- RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning
- From Scoring to Acting: Outcome-Verified Comparative Self-Distillation for LLM Agents
- Self-Distilled RLVR
- Why Does Feedback-Augmented Self-Distillation Fail to Improve Retrieval-Interleaved Search Agents?
- Agentic Reinforcement Learning with Observation-Calibrated Self-Distillation
- ReAct: Synergizing Reasoning and Acting in Language Models
- Agentic Reinforcement Learning with Self-Distilled Reward Shaping
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