Align your fine-tuned model with human preference. Annotate pairs, train a reward model, and run PPO automatically.
Label preference pairs through a simple UI. Mark which response is better — no ML expertise needed.
Langtrain automatically trains a reward model on your labeled pairs using best-in-class RLHF techniques.
Run Proximal Policy Optimization to update your LLM weights to maximize the reward signal. Fully automated.
LangTune automates the full RLHF pipeline — from annotation to PPO training — saving weeks of ML engineering time.