DeReAct divides agent operation among three policies.
Separating agent checks from reasoning improves weaker models’ reliability
DeReAct uses independent policies to screen actions and verify completion against environmental evidence before an agent proceeds or stops.
Big Tech
Ajay Vohra · Tao Chen · Neeti Narayan · Caron Zhang
Amazon · Apple
Research Digest··2 min read
Vohra and colleagues restructure the ReAct agent loop so the model solving a task no longer approves its own actions or decides unilaterally when it has finished.
Why this paper
From Amazon and Apple
In one line
DeReAct improves AI agent reliability by moving action approval and task completion checks out of the main reasoning policy.
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