PPO2 articles

PPO

Articles

Proximal Policy Optimization: Mathematical Foundations, Clipped Surrogate Objectives, and Policy Drift Control in RLHF
Reinforcement learning from human feedback (RLHF) transformed autoregressive large language models from raw next-token predictors into instruction-following assistants. At the computational center of the foundational RLHF pipelines introduced in InstructGPT (Ouyang et al., 2022) is Proximal Policy Optimization (PPO), formulated by John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov at OpenAI in 2017. PPO resolved a fundamental instability in policy gradient methods: th
August 25, 20261 min
Reinforcement Learning from Human Feedback (RLHF): How Reward Models, PPO, and KL Penalties Align LLMs
Pre-training large language models on internet-scale text corpora equips them with general linguistic patterns, world knowledge, and broad reasoning heuristics. However, pre-training optimizes next-token prediction: $\mathbb{E}_{x \sim \mathcal{D}} [\log P_\theta(x_t \mid x_{<t})]$. A base model trained purely on next-token prediction reflects the entirety of its web corpus, reproducing hallucinations, toxic phrasing, incorrect code, and unhelpful conversational patterns. To transform an uncons
August 18, 20261 min