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How LLMs Learn to Be Helpful (RLHF vs DPO)
一文对比RLHF与DPO两种主流大模型训练方法的核心差异与适用场景
In this article, we will look at how that learning actually happens, starting with why instruction-following alone falls short, then walking through t…
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A Unifying Lens on Reward Uncertainty in RLHF
从统一视角剖析RLHF中的奖励不确定性，为强化学习与人类反馈对齐提供新见解
arXiv:2606.09073v1 Announce Type: new Abstract: Reinforcement learning from human feedback (RLHF) is bottlenecked by \emph{reward hacking}, where the …
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Harnesses for Inference-Time Alignment over Execution Trajectories
探索推理时对齐与执行轨迹的新方法，为AI系统行为优化提供理论框架。
arXiv:2605.21516v1 Announce Type: cross Abstract: Harness engineering has emerged as an important inference-time technique for large language model (L…