RLHFlow/RLHF-Reward-Modeling

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1541 stars · 110 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of training recipes for reward models used in RLHF, covering Bradley-Terry reward models, pairwise preference models, ArmoRM, process/outcome-supervised math reward models, and decision-tree reward models. It provides reproducible code, data, and hyperparameters for state-of-the-art reward modeling research.

Use cases

• train a bradley-terry reward model for rlhf
• train a pairwise preference model on human preference data
• build a multi-objective reward model with mixture-of-experts aggregation
• train process-supervised and outcome-supervised reward models for math
• reduce length bias in reward modeling
• reproduce state-of-the-art rewardbench results

When to choose

• you need to train a reward model for RLHF fine-tuning of LLMs
• you want reproducible recipes for multiple reward modeling techniques
• you are doing research on preference modeling or reward hacking mitigation

When to avoid

• you just want to run inference with an existing reward model
• you need a full end-to-end RLHF training pipeline including PPO or DPO
• you are not working with large language models

Facets

library · maturity active

machine-learning llm-training deep-learning large-language-models reinforcement-learning machine-learning artificial-intelligence python rlhf reward-model bradley-terry preference-model prm armorm rewardbench llama3 gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="RLHFlow/RLHF-Reward-Modeling")