This is the Windows app named TTRL whose latest release can be downloaded as v2.0.0sourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
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SCREENSHOTS:
TTRL
DESCRIPTION:
TTRL is an open-source framework for test-time reinforcement learning in large language models, with a particular focus on reasoning tasks where ground-truth labels are not available during inference. The project addresses the problem of how to generate useful reward signals from unlabeled test-time data, and its central insight is that common test-time scaling practices such as majority voting can be repurposed into reward estimates for online reinforcement learning. This makes the framework especially interesting for scenarios where models must keep adapting during evaluation or deployment instead of relying only on fixed pretraining and static fine-tuning. The repository is implemented on top of the verl ecosystem, which allows users to enable TTRL as part of an existing reinforcement learning workflow rather than building a new stack from scratch.
Features
- Online reinforcement learning for unlabeled test-time data
- Reward estimation based on majority-vote style test-time signals
- Integration with the verl reinforcement learning framework
- Scripts for reproducing benchmark experiments such as AIME 2024
- Utilities for converting training data from JSON to Parquet
- Support for multiple models and reasoning benchmarks
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/ttrl.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.