Online Reinforcement Learning for Large Language Models

arXiv:2608.27505v2 Announce Type: cross Abstract: Reinforcement learning from human feedback (RLHF) has become the dominant paradigm for aligning large language models (LLMs) with

arXiv:2605.08354v1 Announce Type: new Abstract: Aligning multimodal generative models with human preferences demands reward signals that respect the compositional,

<p>In the <a href="https://dev.to/rijultp/understanding-reinforcement-learning-with-human-feedback-part-5-training-the-reward-model-with-3g37">previous article</a>, we used loss

arXiv:2605.00123v1 Announce Type: new Abstract: Safety trained large language models (LLMs) can often be induced to answer harmful requests through jailbreak prompts. Because we

arXiv:2604.22167v1 Announce Type: cross Abstract: Language models are increasingly capable and are being rapidly deployed on a population-level scale. As a result, the safety of

<p>57% of organizations estimate their data isn't AI-ready. General-purpose LLMs handle broad tasks well but hallucinate on specialized queries, miss domain jargon, and can't