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Latent-MOPD: Latent Multi-Teacher On-Policy Distillation

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Briefings

Latent-MOPD represents the first representation-level multi-teacher on-policy distillation method for large language models. It integrates multiple specialists by combining their predictions with shared hidden states, eliminating the need for additional teacher training. In a same-family setting, the method outperformed all nine benchmark categories (math, code, logic) on nine datasets, surpassing the best-performing teacher on most benchmarks. Cross-family experiments also showed superior performance compared to single-channel baselines. The approach utilizes late-layer targets and domain-specific grouping to coordinate supervision effectively.

Key points

• Latent-MOPD is the first representation-level multi-teacher on-policy distillation method for large language models, integrating specialists via shared hidden states without additional training.
• In the same-family setting, Latent-MOPD outperformed all nine benchmark categories (math, code, logic) on nine datasets, surpassing the best-performing teacher on most benchmarks.

Context and limitations

Results are limited to the same-family setting and cross-family experiments with separately developed teachers; no data on performance with interleaved teacher domains or specific parameter configurations beyond the main study.

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