environments

Xiaomi has open-sourced over 7,000 reinforcement learning task environments used to train MiMo, enabling developers to train and specialize AI models.

This paper proposes a framework for world models in reinforcement learning by distinguishing between environment, agent, and joint system channels, using computational mechanics to define canonical predictive models and analyzing their complexity under coupling.

Andrej Karpathy frames LLM training as text, conversations, and environments; Prime Intellect's Verifiers is an open-source framework for building and sharing RL environments for LLMs, released under MIT license, with a hub of 2500+ environments.

This paper from UC Berkeley presents practical algorithms for incremental software development environments, addressing how to efficiently update and manage code changes.

OpenReward and TRL now support training on over 350 reinforcement learning environments with minimal code.

An analysis of why RL for coding tasks is gaining traction due to verifiable rewards, and why the emerging framework Harbor addresses the bottleneck of environment complexity in RL training.

OpenEnv, a platform for reinforcement learning environments, is expanding its tutorials, covering topics like evaluating agents, rewards via rubrics, and connecting agents via MCP.