RLHF8 articles

RLHF

Articles

Proximal Policy Optimization (PPO) in LLM Post-Training: Mathematical Foundations of Clipped Surrogate Objectives, Generalized Advantage Estimation, Value Network Architectures, and Adaptive KL Penalties
Proximal Policy Optimization (PPO) in LLM Post-Training: Mathematical Foundations of Clipped Surrogate Objectives, Generalized Advantage Estimation, Value Network Architectures, and Adaptive KL Penalties Reinforcement Learning from Human Feedback (RLHF) established modern alignment for instruction-following foundation models. While pre-training optimizes autoregressive language models to minimize next-token prediction loss over massive text corpora, standard cross-entropy loss cannot directly o
August 27, 20261 min
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
Direct Preference Optimization: Mathematical Derivation, Implicit Reward Formulation, and the Mechanics of RL-Free Alignment
Direct Preference Optimization: Mathematical Derivation, Implicit Reward Formulation, and the Mechanics of RL-Free Alignment Aligning autoregressive large language models with human preferences has traditionally relied on Reinforcement Learning from Human Feedback (RLHF). In the standard formulation popularized by InstructGPT and related post-training regimes, alignment requires a multi-stage pipeline: supervised fine-tuning (SFT), training a separate reward model on pairwise comparison data, a
August 25, 20261 min
Self-Rewarding Language Models: How Iterative DPO and LLM-as-a-Judge Form Autonomous Self-Alignment Loops
Standard post-training alignment pipelines rely on frozen reward models trained on static human feedback datasets. While Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) effectively steer model outputs toward human preferences, they face a fundamental scalability bottleneck: human annotators cannot evaluate superhuman reasoning or generate labels at the scale required for continuous self-improvement. Self-Rewarding Language Models, introduced by Meta AI
August 24, 20261 min
Rejection Sampling Fine-Tuning in Large Language Models: How Best-of-N Filtering, Reward Oracles, and Distillation Align Neural Policies
Rejection Sampling Fine-Tuning: How Filtering Model Outputs by Reward Optimizes Alignment Without Policy Gradient Instability Post-training alignment has become a defining phase in modern large language model development. While supervised fine-tuning (SFT) teaches a model to follow instructions and adopt structured formats, aligning model behavior with human preferences, safety criteria, and domain accuracy requires optimizing against reward signals. Historically, this optimization has been ap
August 23, 20261 min
Distributed RLHF Frameworks in Production: Comparing OpenRLHF, verl, and TRL Architecture, Ray Resource Scheduling, and Rollout-Training Co-Location
Post-training alignment has shifted from offline preference tuning to large-scale, online reinforcement learning. Modern post-training loops for reasoning models, agentic workflows, and conversational alignment require coordinating multiple distinct neural network roles simultaneously. Under standard Proximal Policy Optimization (PPO), an RL infrastructure pipeline must manage up to four distinct model instances: the Actor (the active policy undergoing gradient updates), the Critic (the value mo
August 23, 20261 min
Reward Model Overoptimization in Large Language Models: How Goodhart's Law, Proxy Exploitation, and KL Drift Degrade Alignment
Post-training alignment of large language models relies on optimizing a policy toward objectives defined by human intent and preferences. Because querying human evaluators during every step of continuous reinforcement learning or high-throughput rejection sampling is computationally and logistically infeasible, alignment workflows construct a parameterised proxy reward model. Trained on pairwise preference datasets through formulations such as the Bradley-Terry model, this proxy acts as a surrog
August 22, 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