The authors studied long-horizon tasks in which language model agents modify an environment through sequences of tool calls.
Verified intermediate progress improves reinforcement learning for tool-using agents
ProCredit reruns task acceptance checks after each turn, then credits agents when their tool calls produce measurable progress.
Chinese Tech
Ming Ma · Yi Zhu · Yiran Zhong · Feida Zhu · Chonghan Liu · Pengkun Jiao · +4 more
University of Chinese Academy of Sciences · Institute of Neuroscience, Chinese Academy of Sciences · Tongyi Lab, Alibaba Group · University of California, Los Angeles · Nanyang Technological University
Research Digest··2 min read
Ma and colleagues train agents using changes in verified task progress rather than relying only on final success or failure.
Why this paper
From University of Chinese Academy of Sciences and 4 others
In one line
ProCredit assigns credit to turns based on verified progress checks, improving agent training over outcome-only rewards.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ✓Compute or model size stated (params 4B)
- ·No stated limitations found
- ✓Reports numbers on named benchmarks
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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