A study tracing commit histories across seven open-source reinforcement learning post-training frameworks measures how often published RL methods are actually incorporated into framework code and how they are reviewed. The research frames framework inclusion as the practical path by which a method becomes a configuration change away for practitioners training language models. The source provides no specific inclusion rates, commit counts, or framework names beyond the count of seven.
Practitioners use reinforcement learning methods for language models through training frameworks, so a method that a framework includes is one configuration change away for its users. We measure how often this happens. We trace seven open-source frameworks for RL post-training through their commit h
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