AutoEnvScaling places a proposer agent in a coding sandbox with access to the current solver’s rollouts and online search.
Terminal agents can build adaptive environments for their own training
AutoEnvScaling uses solver rollouts to generate, test and calibrate new reinforcement-learning tasks, then updates the curriculum as the solver improves.
Big Tech
Simon Yu · Baolin Peng · Bo Liu · Zhengyan Shi · Zhaoyang Wang · Hao Cheng · +5 more
Northeastern University · Microsoft · Stanford University · University of Washington · University of North Carolina at Chapel Hill
Research Digest··3 min read
The authors turn reinforcement-learning environment design into a task performed by a terminal agent.
Why this paper
From Microsoft and 4 others
In one line
AutoEnvScaling uses terminal agents to automate RL environment generation, improving performance and enabling recursive self-improvement.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ✓Reports numbers on named benchmarks (3 benchmarks)
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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