Agent Lightning: Train ANY AI Agents with Reinforcement Learning
What this work does
Records agent execution through a common transition interface and separates training from runtime orchestration, decomposing multi-call trajectories for credit assignment.
Connection to discovery
An infrastructure baseline for training an existing scientific-agent harness while preserving tool feedback and call provenance.
Evidence
The inspected architecture includes non-LLM state changes and distinguishes call-level samples from concatenated masked transcripts.
Scope and limitations
A unified trace format does not guarantee that a rendered observation is a sufficient Markov state. Reward validity and credit-assignment assumptions remain domain-specific.
THE RESEARCH CONVERSATION
Discussion & reactions
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