Dense rewards help long-horizon training, but gains vary by task
A new dataset and training recipe aim to make long-horizon AI tasks learnable, yet improvements differ across models and tasks.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Wenbo Hu, Shraman Pramanick, Krishna Kumar Singh, Nanyun Peng, Kai-Wei Chang, Yong Jae Lee
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
What they did and found
Researchers created a large set of 1,170 long-horizon task environments that use dense progress rewards, plus a training method. Open models trained with these signals show gains on some long tasks and better generalization to other benchmarks, but overall performance trails leading models and varies by domain.
Why it matters
Dense progress rewards can speed up learning for complex tasks, but results depend on model size, domain, and setup. Practitioners should temper expectations and test case by case.
The work builds a big library of long tasks across software, data, math and security, with progress checkable at many steps, not just at the end.
Early results show bigger models gain more from dense rewards, but overall performance still trails frontier models and depends on careful domain alignment and quality checks.
What remains uncertain
Results come from synthetic, self-contained tasks; real-world gains depend on how similar tasks are to actual work and on model capabilities.
Original sources · 1
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