'Beyond Validation Loss': best pretraining checkpoint may hurt downstream RL fine-tuning
SongShuran · x · 2026-11-04
A new work, 'Beyond Validation Loss' by @szhanyi, tackles underexplored questions in pretraining science: what makes a good pretrained model, how to evaluate it, when to stop training, and how to optimize for downstream SFT or RL.
Key counterintuitive finding: continued supervised pretraining of generative robot policies can improve reliability (success rate) at the expense of coverage—meaning the best-performing checkpoint by validation loss may not be the best starting point for downstream posttraining. The paper explains why this reliability-coverage tradeoff emerges and how to preserve coverage. SongShuran amplifies the call for more attention to pretraining science.
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