AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making through Multi-Turn Reinforcement Learning
What this work does
Decouples agent execution, environments and RL components, and increases the allowed interaction horizon in stages to balance early exploitation and later exploration.
Connection to discovery
A training infrastructure baseline for horizon curricula and controlled comparisons across discovery environments.
Evidence
The inspected curriculum and framework cover diverse digital environments and multiple policy-gradient estimators rather than one fixed workflow.
Scope and limitations
A modular framework is not a universal discovery objective. Environment resets, reward semantics and the amount of pretraining remain task-dependent despite a no-SFT setting.
THE RESEARCH CONVERSATION
Discussion & reactions
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