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home / papers / 2026 / Prune as You Generate: Online Rollout Pruning for Faster and Better RLVR

Prune as You Generate: Online Rollout Pruning for Faster and Better RLVR

Paper · 2026 · Haobo Xu, Sirui Chen, Ruizhong Qiu et al.

Summary

RLVR methods such as GRPO and DAPO are expensive because they sample many rollouts per prompt. Their learning signal is also often weak: when a group's rewards are almost all correct or almost all wrong, group-normalized advantages approach zero. The authors propose ARRoL (Accelerating RLVR via online RoLlout Pruning), which addresses both issues at once. A lightweight quality head is trained during RL, using the final rewards of finished rollouts as free labels. It estimates the success probability of partially generated rollouts. After a fixed prefix length, low-utility rollouts are dropped so that the survivors have a correctness ratio closer to 0.5, which maximizes reward variance. A system design removes pruned sequences inside the inference engine and then regroups the remaining rollouts into new batches, which are used to compute log-probabilities and update the policy. Experiments use Qwen-3 and LLaMA-3.2 models (1B–8B) on math benchmarks. ARRoL raises average accuracy while training up to 1.7× faster. Its quality head also serves as a weighted voter at test time. The result matters because it shows that fewer, better-balanced rollouts can make RLVR both cheaper and more effective.

Key points

  • With GRPO and DAPO, ARRoL improves average accuracy by +2.30 to +2.99 and gives a 1.6–1.7× end-to-end training speedup. Using the quality head as voting weights in test-time scaling adds up to +8.33 average accuracy over plain majority voting.
  • The quality head predicts final correctness with roughly 80% accuracy (82.37% on Qwen3-1.7B). It outperforms heuristic trace-confidence signals such as DeepConf.
  • Pruning occurs after L_detect = 512 tokens with a keep ratio κ = 0.5. On Qwen-3-1.7B-Base, κ = 0.5 gave the best average accuracy on Math500, MinervaMath and OlympiadBench (33.34) at a 1.61× speedup. κ = 0.25 was faster (2.33×) but less accurate.
  • Speedups by training phase on Qwen-3-1.7B-Base were 1.46× for rollout generation, 1.84× for log-probability computation and 2.08× for model updates. ARRoL also beat random pruning and pushed within-group pass ratios closer to 0.5.
  • Validation covers only mathematical RLVR tasks. Code is available at github.com/Hsu1023/ARRoL.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of Illinois Urbana-ChampaignUrbana · United States184 works · 69,372 citations🏢AmazonSeattle · United States112 works · 40,565 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksInformation Systems · 1,437 works

🧭 Topics 1Research subjects and languages the work is about.Efficient models & compression · 242 works

Cite

Cite as Haobo Xu, Sirui Chen, Ruizhong Qiu, Yuchen Yan, Chen Luo, Monica Xiao Cheng, Jingrui He and Hanghang Tong. 2026. Prune as You Generate: Online Rollout Pruning for Faster and Better RLVR. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.632

@inproceedings{xu2026prune,
  title = {{Prune as You Generate: Online Rollout Pruning for Faster and Better RLVR}},
  author = {Haobo Xu, Sirui Chen, Ruizhong Qiu, Yuchen Yan, Chen Luo, Monica Xiao Cheng, Jingrui He and Hanghang Tong},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.632},
  url = {https://doi.org/10.18653/v1/2026.acl-long.632}
}

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