OpenTinker: Separating Concerns in Agentic Reinforcement Learning

TL;DR

OpenTinker is a modular infrastructure for reinforcement learning of large language model agents, emphasizing separation of concerns to improve flexibility and scalability in agentic learning systems.

Contribution

It introduces a decomposed, component-based framework for RL with LLM agents, enabling flexible configuration and efficient resource management.

Findings

Demonstrates effective RL use cases with large language models.

Shows improved modularity and scalability in agent training.

Validates the framework's practicality in real-world scenarios.

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.