Beyond Cross-Entropy: Pre-training Language Models with Pure Reinforcement Learning
tokenbender · x · 2026-08-01
Traditional language model pre-training relies on cross-entropy, rigidly teaching that only a single token is the correct continuation. The article introduces avatarl, a new paradigm proposing to replace this with a reinforcement learning (RL) framework during pre-training.
- Mechanism: Instead of a single ground truth, a player model learns from a continuous reward signal derived from a pre-trained critic and ground-truth reality.
- Advantage: This allows the model to learn a rich, distributional understanding of language, rewarding plausible alternative tokens rather than just punishing mistakes, leading to deeper language comprehension.
Related event: Explorative Modeling: Third Pretraining Axis, Up to 6x Sampling Efficiency(12 posts)→
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