Ding et al.
Progress-aware advantage shaping resolves uniform credit assignment in agentic RL
ProAS uses a reference graph to mask invalid actions and penalize early deviations, boosting success and convergence.
Chinese Tech
Bowen Ding · Mingda Wan · Yuhan Chen · Jiyao Yuan · Qi Zhu · Futing Wang · +4 more
Zhejiang University · Westlake University · Westlake Institute for Advanced Study · Huawei Noah’s Ark Lab
Research Digest··3 min read
The authors propose ProAS, a plug-and-play framework for critic-free reinforcement learning of LLM agents.
Why this paper
From Huawei Noah’s Ark Lab and 3 others
In one line
Progress-aware advantage shaping improves critic-free LLM-agent training by withholding rewards from invalid actions and concentrating penalties on deviations that cause downstream stagnation.
What we could check
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- ✓Limitations stated by the authors
- ✓Reports numbers on named benchmarks (2 benchmarks)
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