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Armin Dariani

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#machine learning Preprint Aug 2026

One Policy Is Enough: Single-Agent Reinforcement Learning Outperforms Tree Search for Chemistry Tool Learning

This model interleaves reasoning, tool calls, and returns in one left-to-right generation, trained by a supervised warm-up and then outcome-level reinforcement learning against a programmatic reward read directly off the gold call chain, which leaves no learned critic and no judge in the training loop.

Armin Dariani, Sifan Wu, Bang Liu et al. · 0 citations