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Safe-CRL reveals that even minimal failure signals can effectively guide safe policy learning, transforming how we approach reinforcement learning in high-risk environments.
Retaining more copies of low-frequency content while aggressively pruning high-frequency duplicates can significantly boost model performance during pretraining.
A 440MB multilingual translation model now rivals commercial APIs, opening the door for performant on-device translation.
