Pass the Baton: Trajectory-Relayed On-Policy Distillation
Researchers introduce Relay-OPD to fix errors in on-policy distillation where student models fail to recover from incorrect reasoning paths.
- On-policy distillation often fails when student models commit to incorrect reasoning directions.
- Relay-OPD uses teacher-student asymmetry to detect and fix these errors.
- The method reduces computational waste by preventing training on misdirected continuations.
Current on-policy distillation methods often suffer from prefix failure. This occurs when a student model makes an early error in its reasoning, causing all subsequent tokens to be based on that mistake. This leads to unreliable supervision and wasted computational resources during the training process.
To solve this, the researchers developed Relay On-Policy Distillation (Relay-OPD). This method identifies a specific asymmetry where a teacher model attempts to correct a path while the student model continues down the wrong direction. By recognizing this divergence, the system can trigger a label-free handoff to reset the trajectory.
This approach allows for more efficient training by ensuring that the student model does not spend excessive compute attempting to justify incorrect reasoning steps.
Provides a more efficient way to train smaller models using larger teacher models.
Offers a new perspective on error correction in reinforcement learning and distillation.
Improves the reliability of AI reasoning during the training phase.
- On-policy distillation
- A training method where the student model's own generated sequences are used to provide supervision.
- Prefix failure
- A phenomenon where an early error in a sequence causes all subsequent generated tokens to be incorrect.
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