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#RLHF

2 articles — updated daily

RLHF Explained: The Human-Feedback Era Is Already Ending
LLMs

RLHF Explained: The Human-Feedback Era Is Already Ending

RLHF (Reinforcement Learning from Human Feedback) is the training step that turns a raw, pretrained language model into a usable assistant, using human rankings of outputs to train a reward model, then optimizing the base model against that reward model, typically via PPO. It's the technique behind ChatGPT, Claude, and Gemini's instruction-following behavior, but as of mid-2026 the industry has largely moved on to cheaper successors: DPO for general preference tuning, RLAIF for AI-generated feedback, and RLVR for verifiable-reward reasoning models. The bigger signal is that alignment is getting radically cheaper and more DIY, which changes who can afford to do it well. For PMs, that means the competitive moat of "better-behaved AI" is eroding faster than most roadmaps assume, and in-house preference tuning is now within reach of teams that could never have run an RL pipeline.

6 min read →
DPO vs PPO: Direct Preference Optimization Wins Post-Training
Research

DPO vs PPO: Direct Preference Optimization Wins Post-Training

Direct Preference Optimization (DPO) collapses the three-network, reinforcement-learning pipeline behind classic RLHF, a reward model plus PPO, into a single supervised-style loss trained directly on preference pairs. It has become the default preference-alignment method across most open-weight model releases, while frontier labs increasingly pair it with on-policy RL only for reasoning tasks that carry verifiable rewards. The bigger signal is that preference optimization is consolidating into a modular pipeline rather than any one algorithm winning outright. For PMs, which alignment method a vendor or in-house team actually runs says a lot about whether they're optimizing for stability and speed, or holding onto flexibility for the next hard problem.

5 min read →
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