Scaling LLM Reinforcement Learning with Prolonged Training: ProRL v2

NVIDIA Research has developed ProRL v2, an evolution of Prolonged Reinforcement Learning (ProRL), to test the effects of extended RL training on large language models (LLMs). ProRL v2 achieves state-of-the-art performance through advanced algorithms, rigorous regularization, and comprehensive domain coverage. The technique enables RL scaling by exploiting knowledge already possessed by the model and pushing it into genuinely new territory. It provides extended training, stability and robustness, fully verifiable rewards, and brevity enforcement.

NVIDIA Research has developed ProRL v2, an evolution of Prolonged Reinforcement Learning (ProRL), to test the effects of extended RL training on large language models (LLMs). ProRL v2 achieves state-of-the-art performance through advanced algorithms, rigorous regularization, and comprehensive domain coverage. The technique enables RL scaling by exploiting knowledge already possessed by the model and pushing it into genuinely new territory. It provides extended training, stability and robustness, fully verifiable rewards, and brevity enforcement.

ProRL v2 builds on the REINFORCE++ baseline, leveraging Clip-Higher to encourage exploration and Dynamic Sampling to reduce noise and improve learning efficiency. It incorporates several innovations, including scheduled cosine length penalties for producing concise outputs, KL-regularized trust regions with periodic reference resets to the current best checkpoint to help prevent overfitting and ensure stability, and proximal policy optimization with REINFORCE++ baseline. These techniques collectively contribute to sustained improvement and novel solutions in LLMs.

The release of ProRL v2 coincides with ongoing geopolitical tensions and regulatory changes in the tech industry. Chinese authorities have recently advised firms to avoid NVIDIA's H20 chips due to security concerns and domestic semiconductor goals [1]. This regulatory environment adds complexity to the market for AI hardware, with domestic alternatives lagging in AI versatility but still sought in non-government sectors [2]. Despite these challenges, ProRL v2 represents a significant advancement in the field of reinforcement learning and AI capabilities.

References:
[1] https://www.ainvest.com/news/china-urges-firms-avoid-nvidia-h20-chips-security-concerns-push-domestic-semiconductors-2508/
[2] https://developer.nvidia.com/blog/scaling-llm-reinforcement-learning-with-prolonged-training-using-prorl-v2/