AutoEnvScaling: Automating the Data Flywheel with Terminal Agents
Researchers propose AutoEnvScaling, which turns the data flywheel into a terminal task, where a proposer agent builds complete Harbor tasks and sandboxes just like a human researcher. This approach supports self-play reinforcement learning for terminal agents, with the same model continuously improving as both builder and solver, without humans in the loop.
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Introduces AutoEnvScaling, which uses terminal agents to automatically build training environments, so readers can understand the idea behind automating the data flywheel.
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View original postRecursive self-improvement needs agents that can build their own training environments. But researchers still part of the loop, making it hard to scale. We introduce AutoEnvScaling, which turns Data Flywheel into a terminal task: a proposer agent builds complete Harbor tasks and sandbox. It builds just like human researchers. This enables self-play RL for terminal agents: one model improves as both builder and solver, without human-in-the-loop.
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Recursive self-improvement needs agents that can build their own training environments. But researchers still part of the loop, making it hard to scale. We introduce AutoEnvScaling, which turns Data Flywheel into a terminal task: a proposer agent builds complete Harbor tasks and sandbox. It builds just like human researchers. This enables self-play RL for terminal agents: one model improves as both builder and solver, without human-in-the-loop.
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