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A single capable verifier can dramatically improve terminal agent action reliability, achieving higher success rates at lower costs than simply generating more trajectories.
The single mid-run human intervention that pulled the community out of a monoculture is described, what the trace does and does not establish, and the controlled comparison that would settle whether shared research state improves discovery per unit of compute is described.
Molt reduces the cognitive load on researchers by offering a clean and compact codebase that maintains high performance, enabling faster iterations in agentic reinforcement learning.
