facebookresearch/coconut

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1689 stars · 187 forks observed · 2026-08-28

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

Official PyTorch implementation of Coconut, a method for training large language models to reason in a continuous latent space instead of explicit token-level chain-of-thought. It provides training and evaluation pipelines built on Hugging Face models with configurable YAML experiment settings.

Use cases

• train an LLM to reason in continuous latent space
• reproduce the Coconut paper experiments
• run latent chain-of-thought reasoning on GSM8K
• compare CoT vs no-CoT vs latent reasoning training
• fine-tune Hugging Face models with continuous thought steps

When to choose

• you want to experiment with latent-space reasoning for LLMs
• you need the official reference implementation of the Coconut paper
• you are doing research on alternatives to token-based chain-of-thought

When to avoid

• you need a production-ready inference library
• you want plug-and-play reasoning without training models
• you lack GPU resources for LLM fine-tuning

Facets

library · maturity active

llm-training machine-learning rag large-language-models machine-learning deep-learning artificial-intelligence python latent-reasoning chain-of-thought research-code huggingface reasoning linux gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="facebookresearch/coconut")