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CLIFT: Conformal Self-Verification for Web Agent Training and Test-Time Scaling

NeurIPS 2026 · Large language models › Reasoning and chain of thought · approach: Test-time compute scaling · 0 citations (OpenAlex)

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Proposes CLIFT, which certifies URL-conditioned verification questions with a conformal tracker to densify GRPO rewards for web agents and to drive judge-free trajectory selection (CTS) at test time.

Problem

Web-agent RL suffers from sparse binary success rewards and costly frontier-LLM judges that are unavailable at deployment; test-time selection also lacks a judge-free signal.

Approach

A bank of polarity-tagged, URL-scoped natural-language verification questions is certified via Mondrian adaptive conformal tracking against a training-time judge; certified signed trust weights add a non-negative verifier term to per-step rewards and drive a conservative pairwise majority-vote selector over greedy and retry rollouts.

Key results

Surprising findings

Limitations

Experimental setup

Datasets: WebArena Infinity (WAI) training tasks synthesized VisualWebArena (VWA) training tasks synthesized Online Mind2Web (OM2W) rollouts for bank recalibration

Benchmarks: WebArena Infinity VisualWebArena Online Mind2Web

Metrics: success rate weighted average success rate

Baselines: Vanilla GRPO SGV WALT Claude Opus 4.6 Gemini 3 Flash + BU Qwen 3.5+ Kimi K2.5 GPT-5.5 low/high base

Builds on: GRPO Mondrian adaptive conformal inference CEGAR WebJudge

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