CMU Study Shows Blended Training Beats Staged LLM Pipelines

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A new Carnegie Mellon study proposes a unified training rule that blends pretraining, fine-tuning, and RL data. Experiments show this 'data-mixing schedule' outperforms traditional staged pipelines in reasoning and coding while better preserving general skills.

LATEST/A new Carnegie Mellon study proposes a unified training rule that blends pretraining, fine-tuning, and RL data. Experiments show this 'data-mixing schedule' outperforms traditional staged pipelines in reasoning and coding while better preserving general skills.

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Rohan Paul (@rohanpaul_ai)T2Oct 31, 05:52·78 pts
CMU Study Shows Blended Training Beats Staged LLM Pipelines
- Pretraining, fine-tuning, and RL can be unified under a single rule to prevent skill erasure from hard switches
- Blending early-stage data (e.g., 5% midtraining) into RL significantly boosts downstream performance
- Treat training pipelines as data-mixing schedules rather than strict linear stages
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