arXiv AI
Slow-Fast Multi-Teacher On-Policy Distillation for Capability Preservation
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arXiv Machine Learning
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MOPD: Multi-Teacher On-Policy Distillation for Capability Integration in LLM Post-Training
arXiv:2606. 30406v1 Announce Type: cross Abstract: Modern large language models (LLMs) rely on reinforcement learning during post-training to push specific capabilities, yet integrating multiple capabilities into one model remains hard.
arXiv Machine Learning
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Latent-MOPD: Latent Multi-Teacher On-Policy Distillation
Latent-MOPD is a new on‑policy distillation method that allows a single large language model student to learn from multiple specialist teachers by using both the teachers’ output distributions and their hidden state representations. The approach selects late‑layer targets based on teacher‑student relationships, bridges hidden width differences with a shared projection, and groups updates by domain, enabling gradual shift from representation to token supervision. Experiments show that Latent‑MOPD outperforms token‑only, representation‑only, and uniform‑averaging baselines across nine benchmarks in math, code, and logic, and even surpasses the best individual teacher on most tasks.
arXiv AI
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DOPD: Dual On-policy Distillation
arXiv:2606. 30626v1 Announce Type: new Abstract: On-policy distillation (OPD) offers superior capacity transfer by supervising student-sampled trajectories with dense token-level signals.
arXiv AI
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Wisdom of Committee: Diverse Distillation from Large Foundation Models and Domain Experts
arXiv:2402. 14035v4 Announce Type: replace-cross Abstract: Knowledge distillation from foundation models to compact domain models is challenging due to substantial gaps in capacity, architecture, and modality.
arXiv Computation and Language
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Preserving General Capabilities during Domain Specialization with Uncertainty-Calibrated MOPD
The paper introduces uncertainty‑calibrated Multi‑Teacher On‑Policy Distillation (MOPD) to better preserve general language model capabilities while specializing to specific domains. By employing dual‑temperature sampling, positive‑advantage‑density filtering, and centered log‑likelihood filtering, the method selects more informative trajectories and token updates, leading to significant improvements in general‑capability performance on role‑playing and medical‑domain tasks without sacrificing domain performance.
arXiv Machine Learning
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Uni-OPD: Unifying On-Policy Distillation with a Dual-Perspective Recipe
arXiv:2605. 03677v2 Announce Type: replace Abstract: On-policy distillation (OPD) has recently emerged as an effective post-training paradigm for consolidating the capabilities of specialized expert models into a single student model.