AI

DiffGate: Difficulty-Gated Teacher Guidance for On-Policy Distillation

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Briefings

DiffGate improves code and math problem-solving on Qwen3 models by combining group-relative policy optimization with selective teacher guidance. Teacher supervision is applied only to failed trajectories, scaled by group difficulty, to balance dense local guidance with outcome-level supervision. Results show +1.7 to +1.8 points in code avg@8 and +1.1 to +3.9 points in math pass@8 compared to matched GRPO.

Key points

• DiffGate improves code avg@8 by +1.7 points and pass@8 by +1.6 points on Qwen3-0.6B compared to matched GRPO.
• DiffGate improves math pass@8 by +3.9 points on Qwen3-1.7B compared to matched GRPO.

Context

This study addresses limitations in on-policy distillation where existing objectives optimize token-level agreement but miss outcome-level reasoning quality. DiffGate introduces outcome-gated supervision using GRPO, addressing sparse rewards and coarse credit assignment in reinforcement learning.

Limitations

Evaluation protocol only covers four model-domain settings; results are specific to Qwen3 models and may not generalize to other architectures or domains.

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