arXiv:2606.04272v1 Announce Type: new Abstract: The standard LLM training pipeline applies reinforcement learning (RL) only after pre-training and supervised fine-tuning (SFT). We question this status quo by training a LLM from scratch and applying RL, SFT, and SFT followed by RL directly to intermediate pre-training checkpoints. We find that RL is effective very early, and often matches the full SFT$\to$RL pipeline early as well. Through experiments on harder problems, we find that targeted pre-training data composition is a strong lever for RL effectiveness, even more so than model scale. Be
This research emerges as the LLM training paradigm matures, seeking efficiencies and improved performance beyond established post-pre-training RL applications.
It suggests a fundamental re-evaluation of the LLM training pipeline, potentially enabling more efficient and effective model development from earlier stages.
The timing and methods of applying reinforcement learning within the LLM training process could shift significantly, prioritizing data composition over raw scale.
- · AI researchers
- · LLM developers
- · Companies with targeted data sets
- · Developers relying solely on brute-force scaling
- · Standardized LLM training pipeline vendors
Early integration of RL leads to LLMs that are more performant on difficult tasks given specific data composition.
This could democratize LLM development by reducing the reliance on massive compute for final training steps, emphasizing data strategy.
New AI models emerge that are better aligned with complex objectives from their initial training phases, accelerating niche AI applications.
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Read at arXiv cs.LG