Learning from Unverified Peers via Shared Residual Distillation
We propose Shared Residual Distillation (SRD), an on-policy self-distillation method that learns from the model's own unverified peer trajectories.
ProofPaper ↗
Key points
- A language model can generate several solutions to the same problem, some leading to correct answers and others to incorrect ones.
- Many post-training methods turn these trajectories into learning signals using supervision from reference answers, verifiable feedback, or external teachers.
- Can a model improve by learning from its unverified peer solutions without first determining which are correct?
- Controlled ablations support the value of the shared-residual construction, and we observe no degradation relative to the base models on two general-capability benchmarks.
Sources (1)
- [1]Learning from Unverified Peers via Shared Residual DistillationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 03:36 PM
We propose Shared Residual Distillation (SRD), an on-policy self-distillation method that learns from the model's own unverified peer trajectories.
A language model can generate several solutions to the same problem, some leading to correct answers and others to incorrect ones.
Extractive summary: sentences quoted from the sources.
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