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Across several reasoning tasks and model families, the proposed frontier learning approach consistently achieves higher relative gains over fixed-pool baselines, demonstrating that effective post-training requires not only selecting useful problems, but continually generating them at the edge of capability.
Temporal Self-Distillation (TSD), a simple on-policy method that trains dLLMs for fast inference by distilling predictions across time, substantially shifts the speed--quality frontier toward the low-compute regime.
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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