The authors designed a verification-based framework built on partial rollout.
Verification and repair reduce off-policy drift in long-tail RL rollouts
RollVerify actively truncates and regenerates stale trajectory suffixes, matching on-policy accuracy while cutting training cost by over 40%.
Chinese Tech
Yongqiang Yao · Jinru Tan · Kaihuan Liang · Zixin Yin · Yazhe Niu · Ruihao Gong · +2 more
Shanghai Jiao Tong University · Central South University · SenseTime Research · The Chinese University of Hong Kong · Beihang University
Research Digest··3 min read
The authors propose RollVerify, a lightweight framework that augments partial-rollout reinforcement learning for large language models.
Why this paper
From SenseTime Research and 4 others
In one line
RollVerify verifies and repairs partially generated rollouts to maintain accuracy while cutting training costs.
What we could check
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