[2601.21684] Do Not Waste Your Rollouts: Recycling Search Experience for Efficient Test-Time Scaling

Computer Science > Computation and Language arXiv:2601.21684 (cs) [Submitted on 29 Jan 2026 (v1), last revised 5 May 2026 (this version, v2)] Title:Do Not Waste Your Rollouts: Recycling Search Experience for Efficient Test-Time Scaling Authors:Xinglin Wang, Jiayi Shi, Shaoxiong Feng, Peiwen Yuan, Yiwei Li, Yueqi Zhang, Chuyi Tan, Ji Zhang, Boyuan Pan, Yao Hu, Kan Li View a PDF of the paper titled Do Not Waste Your Rollouts: Recycling Search Experience for Efficient Test-Time Scaling, by Xinglin Wang and 10 other authors View PDF Abstract:Test-Time Scaling enhances the reasoning capabilities of Large Language Models by allocating additional inference compute to broaden the exploration of the solution space. However, existing search strategies typically treat rollouts as disposable samples, where valuable intermediate insights are effectively discarded after each trial. This wasted rollout-level experience leads to substantial computational redundancy, as models repeatedly re-derive discovered conclusions and revisit known dead ends across extensive attempts. To bridge this gap, we propose \textbf{Recycling Search Experience (RSE)}, a self-guided, training-free strategy that turns test-time search from a series of isolated trials into a cumulative, experience-guided process. By actively distilling raw trajectories into a shared experience bank, RSE enables positive recycling of intermediate conclusions to shortcut redundant derivations and negative recycling of failure patte...