OpenTinker: Separating Concerns in Agentic Reinforcement Learning
Siqi Zhu, Jiaxuan You
2026-01-13
Summary
This paper introduces OpenTinker, a new system designed to make it easier to train and use large language models (LLMs) as intelligent agents using reinforcement learning.
What's the problem?
Currently, building these kinds of 'agentic' systems – where an LLM learns to make decisions and interact with an environment – is really complicated. Existing methods often combine everything into one big, inflexible process, making it hard to experiment with different learning techniques or efficiently use computing resources. It's like trying to build a car where the engine, wheels, and steering wheel are all one solid piece; changing anything is a huge undertaking.
What's the solution?
OpenTinker solves this by breaking down the process into smaller, independent parts. Think of it like building with LEGOs. You have separate components for the agent (the LLM), the environment it interacts with, and the learning algorithm. OpenTinker provides a central 'scheduler' that manages how these parts work together, handling both training the LLM and letting it make decisions. It can also handle different training methods, like making small adjustments to the LLM or retraining it completely, and it shares computing power efficiently. They also discuss how to expand this to situations with multiple agents.
Why it matters?
This is important because it makes developing LLM-powered agents much more accessible and efficient. By making the system more modular and easier to manage, researchers and developers can experiment more quickly, try out new ideas, and ultimately build more sophisticated and capable AI agents. It lowers the barrier to entry for creating these advanced systems and could speed up progress in the field of artificial intelligence.
Abstract
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.