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Cornell University
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Attention from your LLM can be used to significantly improve preference optimization, outperforming existing methods without needing a separate reward model or heuristic token weighting.
Forget expensive human annotation: this self-play method lets LLMs bootstrap their own training signals for open-ended tasks by generating rubrics to evaluate their own outputs.
