PIVOT mixes teacher corrections into RLVR student rollouts
Original title: PIVOT: Progressive Intervention via Online Teacher Correction
Mean avg@8 on hard logic games rose from 19.5% for the best baseline to 35.6%.
PIVOT addresses sparse rewards in reinforcement learning from verifiable rewards when every rollout in a group fails. It inserts teacher-generated corrections into student trajectories and adds token-level KL supervision on those segments. The authors say either trajectory intervention or token supervision alone can be insufficient, and the combination beats prior methods on logic games and math benchmarks.
- Trajectory intervention seeks reward-bearing paths; token KL gives dense targets.
- The paper analyzes where to intervene and how to supervise those segments.
- Gains are reported on hard logic games and mathematical reasoning benchmarks.
From the original article
1 min read at arxiv.orgReinforcement learning from verifiable rewards (RLVR) is limited by sparse outcome-level feedback on hard reasoning tasks: when all rollouts in a group fail, rewards provide no learning signal. Stronger teachers can provide additional guidance, but this raises a central question: how can such signals be incorporated into RLVR to support successful rollouts while remaining learnable for the student?
By Zhihao Yang · arxiv.org
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