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Self-Play Search Distillation (SPSD), a framework for generating superhuman synthetic data via self-play of MuZero-like networks trained on board games, offers an annotation-efficient way to create high-quality synthetic data for improving LLM performance in reasoning tasks.
Vision-language models (VLMs) are fragile under image corruption. We find that the wording of the question affects VLMs in two opposite ways. Verbose questions make VLMs substantially more robust---e.g., rephrasing"Is there a cat?"into"Please look carefully and answer: is there a cat?". Conversely, VLMs become more fra...
The results suggest that CoT monitoring may be less reliable when preference information arrives through tools or must be inferred from raw artifacts, within the single-call, prefilled-tool setting tested here.
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