PRIME-RL/TTRL

Health v2 · maintenance only

Adoption not part of the score

1124 stars · 80 forks observed · 2026-09-25

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

TTRL is an open-source implementation of Test-Time Reinforcement Learning, a method for training LLMs with RL on unlabeled data using majority voting as the reward signal. It is built on the verl framework and accompanies a NeurIPS 2025 paper.

Use cases

• train an LLM with reinforcement learning without ground-truth labels
• improve math reasoning of a model using only unlabeled test data
• use majority voting as a reward signal for RL fine-tuning
• reproduce TTRL experiments on AIME 2024 with Qwen models
• run online RL on test data with verl integration

When to choose

• you want to fine-tune LLMs on unlabeled reasoning data
• you need a research-grade implementation of test-time RL on verl
• you lack ground-truth labels but have model priors suitable for majority voting

When to avoid

• you need a production RLHF pipeline with human-labeled rewards
• you lack GPU resources for RL training
• your task has no verifiable or majority-votable outputs

Facets

library · maturity active

llm-training reinforcement-learning machine-learning large-language-models machine-learning deep-learning python test-time-reinforcement-learning rlvr majority-voting-reward verl reasoning research-code neurips-2025 gpu linux

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markdown · JSON · MCP: product_card(name="PRIME-RL/TTRL")