Shypd.aiThe largest open index of AI toolsTTRL is a Research & Education tool that investigates Reinforcement Learning (RL) on data without explicit labels for reasoning tasks in Large Language Models (LLMs). It offers an open-source solution for online RL on test data.
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TTRL (Test-Time Reinforcement Learning) is an open-source research project focused on advancing Reinforcement Learning (RL) techniques, particularly for scenarios where ground-truth labels are unavailable during inference. The project investigates how common practices in Test-Time Scaling (TTS), such as majority voting, can generate effective rewards to drive RL training. TTRL has demonstrated significant performance improvements, such as boosting the pass@1 performance of Qwen-2.5-Math-7B by approximately 211% on AIME 2024 using only unlabeled test data. The project provides code and experimental logs, with an implementation based on the 'verl' framework, making it accessible for researchers and developers to reproduce results and further explore test-time reinforcement learning.
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