VPD frames language feedback learning as variational EM so the teacher policy refines itself via trust-region updates on outcomes while the student learns dense token distributions on its own rollouts, outperforming fixed-teacher baselines on reasoning and code tasks.
Reuse your flops: Scaling rl on hard problems by conditioning on very off-policy prefixes
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 4verdicts
UNVERDICTED 4roles
background 1polarities
background 1representative citing papers
REFT improves Pass@1/8/64 in RLVR by uniform first-token sampling from top-N candidates across 0.5B-7B models and multiple difficulty levels.
Prefix Sampling replays self-generated trajectory prefixes to control rollout pass rates near 50% in binary-reward RL, delivering wall-clock speedups and modest performance gains on SWE-bench Verified and AIME tasks.
DenoiseRL optimizes recovery from noisy prefixes in weak-model reasoning failures to improve performance and self-correction on math and general reasoning benchmarks without external supervision.
citing papers explorer
-
Where Rollouts Begin: Low-Load, High-Leverage First-Token Diversification for RLVR
REFT improves Pass@1/8/64 in RLVR by uniform first-token sampling from top-N candidates across 0.5B-7B models and multiple difficulty levels.
-
DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes
DenoiseRL optimizes recovery from noisy prefixes in weak-model reasoning failures to improve performance and self-correction on math and general reasoning benchmarks without external supervision.