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A meta-learning framework for Reinforcement Learning from Human Feedback and Direct Preference Optimization that learns a transferable initialization that enables effective adaptation to a target language with minimal data.
It is shown that RSPO with appropriate regularizers can substantially improve the length-controlled win rate on AlpacaEval-2 across a range of base models, while also achieving consistently superior performance on Arena-Hard, MT-Bench, ArmoRM, and response diversity.
This work introduces SWE-Router, a value-based temporal approach that lets a cheap model run for a few exploratory turns and reads the resulting partial trajectory before deciding whether to continue cheaply or to escalate to an expensive model, and provides a Bayes-optimality theorem showing that conditioning on the p...
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