SARL: Label-Free Reinforcement Learning by Rewarding Reasoning Topology
Reinforcement learning is critical to improving large reasoning models, but its success relies heavily on verifiable rewards (RLVR), making it hard to use in open-ended domains where correctness is ambiguous and cannot be verified. Moreover, reasoning trajectories remain largely unconstrained, and optimizing solely toward the final answer can favor early exploitation over generalization. In this work, we ask whether general reasoning ability can be improved by teaching models how to think (the structure of reasoning) rather than what to produce (the outcome of reasoning), and we extend traditional RLVR to open-ended settings. We introduce Structure-Aware Reinforcement Learning (SARL), a l...
| Authors | Yifan Wang, Bolian Li, David Cho et al. |
|---|---|
| Primary category | cs.AI |
| Published | 30 March 2026 |
| Last updated at source | 10 May 2026 |
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