Paper

The Teacher Is a Direction, Not a Destination: Extrapolating RL-Induced Representation Residuals in On-Policy Distillation

Rather than scaling output-space rewards, it extrapolates the teacher's RL shift in hidden space and trains the student toward beyond-teacher layerwise targets.

arXiv ↗Sep 29, 2026Code ↗

  • llm
  • distillation
  • reinforcement learning
  • representation learning
Method pipeline diagram showing layerwise RL residual regression from teacher to student
Method pipeline diagram showing layerwise RL residual regression from teacher to student

What it is

Extrapolates an RL teacher's hidden-state shift from its base model, instead of logits, for on-policy distillation. This lets the student match or surpass the RL teacher more reliably. Instead of extrapolating teacher improvements in output space via noisy log-prob ratios, it extrapolates the teacher's RL-induced hidden-state residual from the pre-RL base at every layer and regresses the student beyond the teacher along that direction.

Key results

  • Only method whose mean reaches or beats the RL teacher on all 4 pairs
  • Beats OPRD by 0.97-4.06 Avg@16 points across the 4 pairs
  • ExOPD trails the untouched student by 14.1 Avg@16 on Qwen3-4B

RL-induced representation residuals are a more stable and faithful direction for beyond-teacher distillation than output-space log-ratio extrapolation.

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