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About this paper
Alex Stein, Furong Huang, Tom Goldstein
The GATES framework from the University of Maryland, College Park, enables language models to self-improve effectively by using a single model in tutor and student roles, leveraging privileged context and consensus gating to filter self-generated reasoning trajectories. This method achieved an average maj@8 accuracy of 35.4% on public math benchmarks and boosted in-domain student accuracy from 46.0% to 62.0% without relying on external supervision.
Key insights
- Asymmetric context within a single model, where a tutor has privileged information and a student does not, creates an effective dynamic for self-distillation.
- Consensus gating is crucial for filtering unreliable self-generated supervision, preventing the model from reinforcing its own errors.
- Dense, trajectory-level distillation provides a more stable and effective learning signal for complex reasoning capabilities compared to sparse rewards or answer-only supervision.
Summary by alphaXiv.
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