A 4B verifier locates hidden failures and boosts long-horizon agent reliability without retraining
teortaxesTex · x · 2026-09-21
A new arXiv paper shows long-horizon agents rarely catch their first mistake, letting errors silently cascade. Training a 4B step-level verifier to locate failures and select among candidate runs boosts task success rates without any agent retraining — useful for coding, OS world-model and research agents to intercept destructive actions and run best-of-N selection. The reposter adds that agent failures often stem from confused assumptions and rabbitholing into local minima rather than explicit errors, so this behavior must be trained for explicitly.
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