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LLMs miss over 50% of errors in human-written code, but with test-time scaling, they can identify issues in more than 90% of cases鈥攊f you can afford the compute.
MLLMs are surprisingly robust to catastrophic forgetting during fine-tuning, needing only simple regularization or data-hybrid training to maintain performance.
Resource-constrained LLM pretraining gets a boost with PCMind-2.1-Kaiyuan-2B's open-source release and novel techniques for data mixing, repetition, and curriculum learning.
