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This arXiv paper benchmarks ten self-distillation methods against GRPO for agentic reinforcement learning across four environments and three model sizes, using 132 training runs. Its main finding is that none of the methods consistently beats the GRPO baseline, that strong final scores can hide unstable training, and that extra compute does not reliably yield better agents. For ML practitioners, the value is a controlled comparison that challenges reported gains from papers that reused baseline numbers under differing conditions.
More Methods, Better Agents? A Reality Check on Self-Distillation in Agentic Reinforcement Learning
arXiv:2610.19789v1 Announce Type: new Abstract: 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. These limitations obscure the source of reported gains and the practical strengths and weaknesses of competing methods. 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. Our study comprises 132 training runs totaling 12,124.4 recorded H800 GPU-hours, with repeated evaluations over eight rollout seeds for the pretrained backbones and all eleven trained methods. Beyond final task performance, we examine training progress, stability, and measured computational cost. Our evaluation yields three key findings. First, gains over GRPO remain limited in scope, with no method consistently outperforming it across settings. Second, strong final performance can mask unstable training. Third, additional training cost does not consistently translate into better performance. We establish this benchmark to support fair, comprehensive comparison and guide future research toward reliable and cost-effective improvements in agent training. Project page: https://aim-uofa.github.io/Self-Distillation-AgentRL/
Title:More Methods, Better Agents? A Reality Check on Self-Distillation in Agentic Reinforcement Learning
Abstract: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. These limitations obscure the source of reported gains and the practical strengths and weaknesses of competing methods. 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. Our study comprises 132 training runs totaling 12,124.4 recorded H800 GPU-hours, with repeated evaluations over eight rollout seeds for the pretrained backbones and all eleven trained methods. Beyond final task performance, we examine training progress, stability, and measured computational cost. Our evaluation yields three key findings. First, gains over GRPO remain limited in scope, with no method consistently outperforming it across settings. Second, strong final performance can mask unstable training. Third, additional training cost does not consistently translate into better performance. We establish this benchmark to support fair, comprehensive comparison and guide future research toward reliable and cost-effective improvements in agent training. Project page: this https URL
Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2610.19789 [cs.CL]
(or arXiv:2610.19789v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.19789 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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