Learning Structured Reasoning via Tractable Trajectory Control
Large language models can exhibit emergent reasoning behaviors, often manifested as recurring lexical patterns (e.g., "wait," indicating verification). However, complex reasoning trajectories remain sparse in unconstrained sampling, and standard RL often fails to guarantee the acquisition of diverse reasoning behaviors. We propose a systematic discovery and reinforcement of diverse reasoning patterns through structured reasoning, a paradigm that requires targeted exploration of specific reasoning patterns during the RL process. To this end, we propose Ctrl-R, a framework for learning structured reasoning via tractable trajectory control that actively guides the rollout process, incentiviz...
| Authors | Po-Nien Kung, Zhen Yang, Jeffrey Luo et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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