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   Reality check: self-distillation methods in agentic RL show limited gains over GRPO

   A new arXiv paper offers a reality check on self-distillation in agentic reinforcement learning. The authors compared ten representative methods against a GRPO baseline on ALFWorld, ScienceWorld, Search-QA and WebShop across three model sizes, running 132 training runs that totaled 12,124.4 recorded H800 GPU-hours, with repeated evaluations over eight rollout seeds for the pretrained backbones and all eleven trained methods. They report three findings: gains over GRPO remain limited in scope, with no method consistently outperforming it; strong final performance can mask unstable training; and additional training cost does not consistently translate into better performance.

   Hugging Face · Papers · 🔥 0

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