openreasoner/openr

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1853 stars · 132 forks observed · 2026-08-28

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

OpenR is an open-source Python framework that integrates search, reinforcement learning, and process supervision to improve chain-of-thought reasoning in large language models. It provides data pipelines, PRM training, MCTS-style search strategies, and test-time scaling experiments, accompanied by released datasets and models.

Use cases

• train a process reward model for LLM reasoning steps
• run MCTS search over reasoning chains with an LLM
• fine-tune LLMs with online reinforcement learning for math reasoning
• generate reasoning step data from outcome labels automatically
• study test-time scaling laws for LLM inference
• improve model accuracy on math benchmarks like MATH

When to choose

• you want an open-source reproduction of o1-style reasoning with RL and process supervision
• you need to train or evaluate process reward models
• you want to experiment with search strategies like MCTS at inference time
• you need reasoning datasets and pretrained PRMs to bootstrap research

When to avoid

• you just need a general LLM inference or serving framework
• your task is simple prompting without multi-step reasoning
• you lack GPU resources for RL training and large-scale search
• you need a production chatbot rather than a research framework

Facets

framework · maturity active

llm-training machine-learning reinforcement-learning rag benchmarking data-generation large-language-models machine-learning reinforcement-learning artificial-intelligence python cross-platform process-supervision prm mcts test-time-compute reasoning math-reasoning reinforcement-learning search-strategies natural-language-processing gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="openreasoner/openr")