BytedTsinghua-SIA/DAPO

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1861 stars · 85 forks observed · 2026-08-28

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

DAPO is an open-source reinforcement learning system for large-scale LLM training, released by ByteDance Seed and Tsinghua AIR. It implements the Decoupled Clip and Dynamic Sampling Policy Optimization algorithm on top of the verl framework, with code, datasets, and model weights achieving 50% on AIME 2024.

Use cases

• train llm with reinforcement learning
• reproduce dapo rl training on qwen models
• run rlhf-style policy optimization for math reasoning
• evaluate a 32b model on aime 2024
• research scalable rl for large language models

When to choose

• you want to reproduce or extend state-of-the-art LLM RL training
• you need the DAPO algorithm, dataset, and checkpoints together
• you already use the verl framework and want proven RL recipes

When to avoid

• you need a production-ready training platform with support guarantees
• you lack multi-GPU infrastructure for large-scale RL
• you need a permissively licensed codebase, since no license is specified

Facets

library · maturity active

llm-training reinforcement-learning machine-learning benchmarking large-language-models reinforcement-learning deep-learning machine-learning python rlhf policy-optimization verl math-reasoning aime research-code gpu linux docker

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markdown · JSON · MCP: product_card(name="BytedTsinghua-SIA/DAPO")