Think Longer to Explore Deeper: Learn to Explore In-Context via Length-Incentivized Reinforcement Learning
Authors: name: Futing Wang, name: Jianhao Yan, name: Yun Luo, name: Ganqu Cui, name: Zhi Wang, name: Xiaoye Qu, name: Yue Zhang, name: Yu Cheng, name: Tao Lin
Organization: Westlake University

Achieving effective test-time scaling requires models to engage in In-Context Exploration -- the intrinsic ability to generate, verify, and refine multiple reasoning hypotheses within a single continuous context.
  Grounded in State Coverage theory, our analysis identifies a critical bottleneck to enabling this capability: while broader state coverage requires longer reasoning trajectories, the probability of sampling such sequences decays exponentially during autoregressive generation, a phenomenon we term the ``Shallow Exploration Trap''.
  To bridge this gap, we propose Length-Incentivized Exploration(\method).
  This simple yet effective recipe explicitly encourages models to explore more via a length-based reward coupled with a redundancy penalty, thereby maximizing state coverage in two-step manner.
  Comprehensive experiments across different models (Qwen3, Llama) demonstrate that \method effectively incentivize in-context exploration.
  As a result, our method achieves an average improvement of 4.4\% on in-domain tasks and a 2.7\% gain on out-of-domain benchmarks.