Lightning OPD: Efficient Post-Training for Large Reasoning Models with Offline On-Policy Distillation

Lightning OPD is an efficient offline framework for large language model distillation that enforces teacher consistency by precomputing log-probabilities, eliminating the need for a live teacher server during training. This method achieves state-of-the-art performance while significantly reducing computational overhead and infrastructure costs, making advanced post-training techniques more accessible to researchers.

Read the full article at arXiv cs.LG (ML)

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