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<div class="nav-links"><a href="#method">Method</a><a href="#results">Results</a><a class="nav-cite" href="#citation">Citation</a><a href="https://arxiv.org/abs/2610.06829" target="_blank" rel="noopener">Read paper <span class="external" aria-hidden="true">↗</span></a></div>
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<div class="eyebrow"><span class="dot"></span>Salesforce AI Research · October 2026</div>
<div class="wordmark" aria-hidden="true">CLIFT</div>
<h1>Conformal Self-Verification for Web Agent Training and Test-Time Scaling</h1>
<p class="hero-copy">Turn judge feedback into a reusable bank of certified questions that helps web agents learn and choose better trajectories.</p>
<div class="resources"><a class="button primary" href="https://arxiv.org/abs/2610.06829" target="_blank" rel="noopener">Read the paper <span aria-hidden="true">↗</span></a><a class="button" href="https://arxiv.org/pdf/2610.06829" target="_blank" rel="noopener">PDF <span aria-hidden="true">↓</span></a><a class="button" href="#citation">BibTeX</a></div>
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<figure class="hero-art" aria-label="CLIFT concept overview" style="margin:0">
<div class="art-label"><span>Learn what to verify</span><span>Reuse at test time</span></div>
<img src="assets/intro.svg" alt="Original CLIFT concept figure from the paper, connecting judge feedback, self-verification, training and test-time selection." width="572" height="270">
<figcaption class="art-caption"><span>One certified bank</span><span>Training → selection → transfer</span></figcaption>
</figure>
</div>
<div class="authors"><div class="author-list"><span>Yifan Zhang</span><span>Yutong Dai</span><span>Viraj Prabhu</span><span>Zhiyuan Hu</span><span>Ran Xu</span><span>Zeyuan Chen</span></div><div class="affiliation">Salesforce AI Research</div></div>
</header>
<section class="metrics" aria-label="Headline benchmark results">
<div class="metric"><div class="metric-label">WebArena Infinity</div><div class="metric-value">74.6<span>%</span></div><div class="metric-note"><span class="gain">+12.8 pp</span> vs. base Gemma-4 · K = 3</div></div>
<div class="metric"><div class="metric-label">VisualWebArena</div><div class="metric-value">53.7<span>%</span></div><div class="metric-note">Bank transfer to GPT-5.5 · K = 4</div></div>
<div class="metric"><div class="metric-label">Online Mind2Web</div><div class="metric-value">61.0<span>%</span></div><div class="metric-note"><span class="gain">+11.3 pp</span> vs. GPT-5.5 high effort · K = 4</div></div>
</section>
<section class="section" id="method">
<div class="section-head"><div><div class="section-number">01 / THE METHOD</div><h2>Feedback that lasts<br>beyond training.</h2></div><p>Binary outcomes give sparse training signals. Language-model judges provide richer feedback, but their supervision is costly. CLIFT converts that feedback into URL-scoped verification questions with calibrated trust weights, then reuses the same bank during training and inference.</p></div>
<figure class="method-figure"><div class="method-scroll"><img src="assets/method-overview.svg" alt="CLIFT method overview: a comparative judge and self-verifier score training rollouts; the Compositional Conformal Certifier certifies URL-scoped question weights for rewards. The frozen bank then provides evidence for conservative test-time trajectory selection." loading="lazy" width="1255" height="510"></div><figcaption>Figure 2 from the paper. Training uses judge feedback and certified per-step evidence. At test time, the frozen bank supports Conformal Trajectory Selection (CTS) without an external judge. On smaller screens, scroll horizontally to inspect the figure.</figcaption></figure>
<div class="steps">
<article class="step"><div class="step-top"><span class="step-n">1</span><h3>Certify the questions</h3></div><p>The Compositional Conformal Certifier checks agreement with judge feedback at URL-path, host, and global tiers. Reliable questions receive polarity-aware trust weights.</p></article>
<article class="step"><div class="step-top"><span class="step-n">2</span><h3>Improve credit assignment</h3></div><p>Certified evidence adds a non-negative bonus to per-step judge rewards. URL-stratified advantages guide the GRPO policy update.</p></article>
<article class="step"><div class="step-top"><span class="step-n">3</span><h3>Select with repeated evidence</h3></div><p>CTS compares completed trajectories using the frozen bank. A retry replaces the incumbent only after a majority of verifier votes supports the swap; ties keep the incumbent.</p></article>
</div>
</section>
<section class="section" id="results">
<div class="section-head"><div><div class="section-number">02 / THE EVIDENCE</div><h2>Three settings.<br>One reusable bank.</h2></div><p>Train an open web agent, transfer verification knowledge to a closed model, and adapt the bank to live websites. Every CTS result emits one selected trajectory per task; K is the candidate rollout budget.</p></div>
<div class="results-panel">
<div class="tabs" role="tablist" aria-label="Benchmark results"><button class="tab" id="tab-wai" role="tab" aria-selected="true" aria-controls="panel-wai" tabindex="0">WebArena Infinity</button><button class="tab" id="tab-vwa" role="tab" aria-selected="false" aria-controls="panel-vwa" tabindex="-1">VisualWebArena</button><button class="tab" id="tab-om2w" role="tab" aria-selected="false" aria-controls="panel-om2w" tabindex="-1">Online Mind2Web</button></div>
<div class="result-content" id="panel-wai" role="tabpanel" aria-labelledby="tab-wai">
<div class="result-topline"><div><h3>Stronger training on an open backbone</h3><p>Task-weighted success rate on the public 9-app comparison set. CLIFT trains Gemma-4 31B with LoRA; CTS adds inference-time selection.</p></div><span class="pill">OPEN MODEL · TRAIN + SELECT</span></div>
<div class="chart" role="img" aria-label="WAI success rates on a 50–80% axis: Gemma-4 base 61.8%, vanilla GRPO 64.7%, CLIFT 72.6%, CLIFT plus CTS K=3 74.6%.">
<div class="chart-axis" aria-hidden="true"><span>50%</span><span>60%</span><span>70%</span><span>80%</span></div>
<div class="bar-row"><div class="bar-label">Gemma-4 base</div><div class="bar-track"><div class="bar-fill" style="width:39.333333%"></div></div><div class="bar-value">61.8</div></div>
<div class="bar-row"><div class="bar-label">Vanilla GRPO</div><div class="bar-track"><div class="bar-fill" style="width:49%"></div></div><div class="bar-value">64.7</div></div>
<div class="bar-row"><div class="bar-label">CLIFT</div><div class="bar-track"><div class="bar-fill" style="width:75.333333%"></div></div><div class="bar-value">72.6</div></div>
<div class="bar-row featured"><div class="bar-label">CLIFT + CTS (K = 3)</div><div class="bar-track"><div class="bar-fill" style="width:82%"></div></div><div class="bar-value">74.6</div></div>
</div><div class="axis-label">Success rate (%) · axis range 50–80% · higher is better</div>
<div class="result-foot">Source: <a href="https://arxiv.org/html/2610.06829v1#S4.T6" target="_blank" rel="noopener">arXiv v1, Table 6</a>. Training rows share the comparative judge and browser-use backbone. Training contributes +10.8 pp; CTS adds +2.0 pp.</div>
</div>
<div class="result-content" id="panel-vwa" role="tabpanel" aria-labelledby="tab-vwa" hidden>
<div class="result-topline"><div><h3>Transfer the bank to a closed model</h3><p>Environment-size-weighted success rate across Classifieds, Shopping, and Reddit. GPT-5.5 weights remain frozen; the bank learned during open-model training is reused at inference.</p></div><span class="pill">FROZEN GPT-5.5 · K = 4</span></div>
<div class="chart" role="img" aria-label="VWA weighted success rates: GPT-5.5 plus CLIFT 48.5%, GPT-5.5 plus CLIFT plus CTS K=4 53.7%. Published references: SGV 50.2% and WALT 52.9%."><div class="chart-axis" aria-hidden="true"><span>0</span><span>50</span><span>100%</span></div>
<div class="bar-row"><div class="bar-label">SGV (published reference)</div><div class="bar-track"><div class="bar-fill" style="width:50.2%"></div></div><div class="bar-value">50.2</div></div>
<div class="bar-row"><div class="bar-label">WALT (published reference)</div><div class="bar-track"><div class="bar-fill" style="width:52.9%"></div></div><div class="bar-value">52.9</div></div>
<div class="bar-row"><div class="bar-label">GPT-5.5 + CLIFT</div><div class="bar-track"><div class="bar-fill" style="width:48.5%"></div></div><div class="bar-value">48.5</div></div>
<div class="bar-row featured"><div class="bar-label">GPT-5.5 + CLIFT + CTS</div><div class="bar-track"><div class="bar-fill" style="width:53.7%"></div></div><div class="bar-value">53.7</div></div>
</div><div class="axis-label">Success rate (%) · higher is better</div><div class="result-foot">Source: <a href="https://arxiv.org/html/2610.06829v1#S4.T4" target="_blank" rel="noopener">arXiv v1, Table 4</a>. The final CTS row uses K = 4 and returns one trajectory per task. Published references are separate systems as reported in the paper.</div>
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<div class="result-content" id="panel-om2w" role="tabpanel" aria-labelledby="tab-om2w" hidden>
<div class="result-topline"><div><h3>Adapt verification to the live web</h3><p>Realised success on 300 Online Mind2Web tasks. The question bank is translated, expanded, and recalibrated on OM2W rollouts; policy weights receive no OM2W-specific fine-tuning.</p></div><span class="pill">BANK TRANSFER · LIVE WEB</span></div>
<div class="chart" role="img" aria-label="Online Mind2Web success rates: Gemma-4 base 40.0%, Gemma-4 plus transferred bank and CTS 45.0%, GPT-5.5 high effort 49.7%, GPT-5.5 high effort plus transferred bank and CTS K=4 61.0%."><div class="chart-axis" aria-hidden="true"><span>0</span><span>50</span><span>100%</span></div>
<div class="bar-row"><div class="bar-label">Gemma-4 base</div><div class="bar-track"><div class="bar-fill" style="width:40%"></div></div><div class="bar-value">40.0</div></div>
<div class="bar-row"><div class="bar-label">Gemma-4 + bank + CTS</div><div class="bar-track"><div class="bar-fill" style="width:45%"></div></div><div class="bar-value">45.0</div></div>
<div class="bar-row"><div class="bar-label">GPT-5.5 high effort</div><div class="bar-track"><div class="bar-fill" style="width:49.7%"></div></div><div class="bar-value">49.7</div></div>
<div class="bar-row featured"><div class="bar-label">GPT-5.5 high + bank + CTS</div><div class="bar-track"><div class="bar-fill" style="width:61%"></div></div><div class="bar-value">61.0</div></div>
</div><div class="axis-label">Success rate (%) · higher is better</div><div class="result-foot">Source: <a href="https://arxiv.org/html/2610.06829v1#S4.SS3" target="_blank" rel="noopener">arXiv v1, §4.3 and Figure 5</a>. The final GPT-5.5 row uses K = 4. Bank adaptation includes OM2W-specific questions and held-out rollout recalibration; no policy training on OM2W.</div>
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<div class="insight"><strong>+12.8 pp</strong><p>CLIFT + CTS improves Gemma-4 over its base WAI success rate. The gain combines stronger policy training with conservative trajectory selection.</p></div>
</section>
<section class="section abstract" id="overview"><h2>The core idea</h2><div><p>A trajectory score is used once. A certified question bank can be inspected, recalibrated, and reused. CLIFT makes that bank the shared interface between expensive judge supervision and an agent’s own rollouts: it supplies dense evidence during reinforcement learning and structured evidence for test-time selection. Across the evaluated settings, the bank supports open-model training, transfer to a frozen closed model, and adaptation to live websites.</p><p style="margin-top:16px"><a href="https://arxiv.org/html/2610.06829v1" target="_blank" rel="noopener">Explore the full method, protocols, and ablations ↗</a></p></div></section>
<section class="section" id="citation"><div class="citation"><div class="citation-top"><h2>Cite CLIFT</h2><button type="button" class="copy-button" id="copy-citation">Copy BibTeX</button></div><pre id="bibtex">@misc{zhang2026clift,
title={CLIFT: Conformal Self-Verification for Web Agent Training and Test-Time Scaling},
author={Yifan Zhang and Yutong Dai and Viraj Prabhu and Zhiyuan Hu and Ran Xu and Zeyuan Chen},
year={2026},
eprint={2610.06829},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2610.06829}
}</pre><p class="copy-status" id="copy-status" role="status" aria-live="polite"></p></div></section>
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<footer class="footer"><div class="wrap footer-inner"><span>CLIFT · Salesforce AI Research · 2026</span><div class="footer-links"><a href="https://arxiv.org/abs/2610.06829" target="_blank" rel="noopener">Paper</a><a href="https://arxiv.org/html/2610.06829v1" target="_blank" rel="noopener">Results source: arXiv v1</a><a href="#top">Back to top ↑</a></div></div></footer>
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