VeriFine organizes training into two coupled loops.
Adaptive judges help embodied agents improve as failure patterns change
VeriFine jointly updates an embodied policy, its training curriculum and its evaluator when fixed verification begins to constrain learning.
Big Tech
Zewei Zhou · Rachel Luo · Yulong Cao · Chaowei Xiao · Chensheng Peng · Boyi Li · +7 more
NVIDIA · UCLA · UC Berkeley · Stanford University
Research Digest··3 min read
Zhou and colleagues introduce VeriFine, an agent framework designed to prevent self-improvement from stalling when an evaluator can no longer reliably recognize a policy’s emerging errors.
Why this paper
From NVIDIA and 3 others
In one line
VeriFine co-evolves policy, curriculum, and judge to scale verification and enable continuous self-improvement in embodied reasoning.
What we could check
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