The authors generated tasks backward from a complete hidden specification, called a latent world.
Dense progress rewards help train agents on longer computer tasks
The authors construct 1,170 verifiable environments from hidden workflow specifications, then use intermediate progress to provide reinforcement-learning rewards before tasks are completed.
Big Tech
Wenbo Hu · Shraman Pramanick · Krishna Kumar Singh · Nanyun Peng · Kai-Wei Chang · Yong Jae Lee
Adobe Research · University of California, Los Angeles
Research Digest··3 min read
Hu et al.
Why this paper
From Adobe Research and University of California, Los Angeles
In one line
Latent-world task generation plus dense progress rewards makes long-horizon agent training tractable and improves open models across in-domain and transferred terminal benchmarks.
What we could check
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- ·No stated limitations found
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