ProAS: A Progress-Aware Advantage Shaping Framework for Multi-Turn Agentic Reinforcement Learning
ProAS is a plug-and-play framework for critic-free reinforcement learning of LLM agents. It addresses uniform credit assignment in GRPO-style methods, which broadcasts trajectory-level advantages equally to all actions. Using a dynamically evolving Agentic Reference Graph, it applies Masking Unreasonable Incentives and Asymmetric Penalty Redistribution. Experiments on Qwen2.5-1.5B-Instruct and Qwen2.5-7B-Instruct report peak success rates of 91.9% and 94.3% on ALFWorld, 66.7% and 70.8% on WebShop, and convergence up to 2.44 times faster.
Open original source huggingface.coOne primary source; other links are context, not independent corroboration.
- Primary sourceHugging Face Daily Papers
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- Linked in sourceView arXiv page
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ProAS is a plug-and-play framework for critic-free reinforcement learning of LLM agents. It addresses uniform credit assignment in GRPO-style methods, which broadcasts trajectory-level advantages equally to all actions. Using a dynamically evolving Agentic Reference Graph, it applies Masking Unreasonable Incentives and Asymmetric Penalty Redistribution. Experiments on Qwen2.5-1.5B-Instruct and Qwen2.5-7B-Instruct report peak success rates of 91.9% and 94.3% on ALFWorld, 66.7% and 70.8% on WebShop, and convergence up to 2.44 times faster.
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Source claims not independently checked; results are reported on two benchmarks and two Qwen2.5 model sizes.
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Key findings from the stored brief
- Uniform trajectory-level advantage broadcasting reinforces structurally unreasonable actions in preferred trajectories.
- ProAS uses an Agentic Reference Graph with Masking Unreasonable Incentives and Asymmetric Penalty Redistribution.
- Reported peak success rates reach 91.9% and 94.3% on ALFWorld for the two Qwen2.5 model sizes.
- Training convergence is reported to accelerate by up to 2.44 times, with robustness to imperfect or distilled graphs.
Source claims not independently checked; results are reported on two benchmarks and two Qwen2.5 model sizes.
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Why this may matter
Critic-free RL is attractive for training LLM agents because it avoids critic overhead. Finer per-action credit assignment could improve both effectiveness and training efficiency in multi-turn agentic settings if the reported gains hold.
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Source claims not independently checked; results are reported on two benchmarks and two Qwen2.5 model sizes.
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