UnifiedPlayers divides training among a Planning Player that generates tasks, an Execution Player that produces multi-turn reasoning trajectories with Python calls, and an Evaluation Player that writes executable verifiers.
Co-trained agent roles improve tool-based reasoning and verification
UnifiedPlayers jointly trains task generation, Python-assisted execution, and executable verification rather than relying on a fixed evaluator.
Industry
Wenjie Liao · Liangjie Zhao · Zehong Cao
Waseda University · Adelaide University
Research Digest··2 min read
Thread:Agent Self-Improvement
Liao, Zhao, and Cao developed a cooperative reinforcement-learning framework in which three specialized players continually generate tasks, solve them with tools, and construct verifiers.
Why this paper
From Waseda University and Adelaide University · Part of Agent Self-Improvement, now 10 papers
In one line
Jointly training planning, execution, and evaluation players under GRPO improves tool-integrated reasoning, beating prior baselines on mathematical and general reasoning benchmarks.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ·No benchmark numbers found
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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