More Methods, Better Agents? A Reality Check on Self-Distillation in Agentic Reinforcement Learning
Self-distillation in agentic reinforcement learning has produced a growing number of methods.
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Key points
- However, existing evaluations often reuse published baseline scores under differing experimental conditions, provide few direct comparisons among contemporaneous methods, and give limited attention to training stability and computational cost.
- Costly agent training further motivates a common basis for comparison.
- To address these gaps, we compare ten representative methods against GRPO on ALFWorld, ScienceWorld, Search-QA, and WebShop across three model sizes.
- Beyond final task performance, we examine training progress, stability, and measured computational cost.
Sources (2)
- [1]More Methods, Better Agents? A Reality Check on Self-Distillation in Agentic Reinforcement LearningarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 08:36 AM
Self-distillation in agentic reinforcement learning has produced a growing number of methods.
However, existing evaluations often reuse published baseline scores under differing experimental conditions, provide few direct comparisons among contemporaneous methods, and give limited attention to training stability and computational cost.
- [2]More Methods, Better Agents? A Reality Check on Self-Distillation in Agentic Reinforcement LearningHugging Face Daily Papers · Oct 14, 12:00 AM · same content
Extractive summary: sentences quoted from the sources.
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