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This research presents OPD2, a novel distillation technique that focuses on the learning trajectory of reasoning models by using a delta signal between teacher and base models. Experimental results prove it outperforms standard on-policy distillation across multiple complex reasoning domains.

This research presents OPD2, a novel distillation technique that focuses on the learning trajectory of reasoning models by using a delta signal between teacher and base models. Experimental results prove it outperforms standard on-policy distillation across multiple complex reasoning domains.

This study demonstrates that reinforcement learning post-training for LLMs is not a uniform process across all parameters but is concentrated in specific middle layers. By leveraging this discovery, researchers developed layer-aware training methods that outperform traditional full-parameter optimization.

The researchers present Program-as-Weights (PAW), a paradigm that shifts LLM usage from expensive per-input API calls to a 'compile-once, run-locally' model using neural adapters. This approach allows small, specialized models to outperform massive foundation models on specific fuzzy tasks while maintaining high efficiency and privacy.

This study demonstrates that reinforcement learning post-training for LLMs is not a uniform process across all parameters but is concentrated in specific middle layers. By leveraging this discovery, researchers developed layer-aware training methods that outperform traditional full-parameter optimization.

The researchers present Program-as-Weights (PAW), a paradigm that shifts LLM usage from expensive per-input API calls to a 'compile-once, run-locally' model using neural adapters. This approach allows small, specialized models to outperform massive foundation models on specific fuzzy tasks while maintaining high efficiency and privacy.