ProAS
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- ProAS targets finer credit assignment in agentic reinforcement learning
The ProAS paper proposes a critic-free framework for fine-grained credit assignment in multi-turn LLM agent reinforcement learning. It uses a dynamic Agentic Reference Graph, Masking Unreasonable Incentives and Asymmetric Penalty Redistribution to avoid assigning trajectory-level advantages uniformly across actions. The authors report peak success rates of 91.9% and 94.3% on ALFWorld for Qwen2.5-1.5B-Instruct and Qwen2.5-7B-Instruct, and 66.7% and 70.8% on WebShop, with convergence accelerated by up to 2.44 times.
Hugging Face · Papers · 🔥 0
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