OpenTinker: Separating Concerns in Agentic Reinforcement Learning
This addresses the problem of building scalable and flexible agentic learning systems for researchers and practitioners, but it is incremental as it focuses on infrastructure rather than algorithmic breakthroughs.
The authors tackled the complexity of reinforcement learning for large language model agents by introducing OpenTinker, an infrastructure that separates concerns into composable components, resulting in a managed system for training and inference workloads.
We introduce OpenTinker, an infrastructure for reinforcement learning (RL) of large language model (LLM) agents built around a separation of concerns across algorithm design, execution, and agent-environment interaction. Rather than relying on monolithic, end-to-end RL pipelines, OpenTinker decomposes agentic learning systems into lightweight, composable components with clearly defined abstraction boundaries. Users specify agents, environments, and interaction protocols, while inference and training are delegated to a managed execution runtime. OpenTinker introduces a centralized scheduler for managing training and inference workloads, including LoRA-based and full-parameter RL, supervised fine-tuning, and inference, over shared resources. We further discuss design principles for extending OpenTinker to multi-agent training. Finally, we present a set of RL use cases that demonstrate the effectiveness of the framework in practical agentic learning scenarios.