RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments
arXiv:2511.07317v2 Announce Type: replace-cross Abstract: We introduce Reinforcement Learning (RL) with Adaptive Verifiable Environments (RLVE), an approach using verifiable environments that procedurally generate problems and provide algorithmically verifiable rewards, to scale up RL for language models (LMs). RLVE enables each verifiable environment to dynamically adapt its problem difficulty distribution to the policy model's capabilities as training progresses. In contrast, static data distributions often lead to vanishing learning signals when problems are either too easy or too hard for
The continuous drive to improve large language models requires more efficient and scalable training methods, especially as model sizes and complexity increase.
This development offers a significant step towards more robust and generalizable AI, potentially accelerating development cycles and the deployment of advanced language models in real-world applications.
Reinforcement learning for language models can now be scaled more effectively, allowing for adaptive difficulty in training environments and reducing the problem of vanishing learning signals.
- · AI research labs
- · Large language model developers
- · AI platform providers
- · Developers reliant on static, less efficient training methods
- · Companies with limited compute resources
More sophisticated and capable language models will emerge at a faster pace.
The cost and time required to train highly effective LMs could decrease, democratizing access to advanced AI capabilities.
This could accelerate the development of autonomous AI agents capable of solving complex, verifiable problems.
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Read at arXiv cs.LG