How2Everything: Mining the Web for How-To Procedures to Evaluate and Improve LLMs

• URL: http://arxiv.org/abs/2602.08808v1
• Date: Mon, 09 Feb 2026 15:47:14 GMT
• Title: How2Everything: Mining the Web for How-To Procedures to Evaluate and Improve LLMs
• Authors: Yapei Chang, Kyle Lo, Mohit Iyyer, Luca Soldaini,
• Abstract summary: How2Everything is a framework to evaluate and improve goal-conditioned procedure generation.<n>Our framework includes How2Mine, which mines 351K procedures from 980K web pages across 14 topics.<n>How2Score is an evaluation protocol that uses an LLM judge to detect whether a generation contains any critical failure that would prevent achieving the goal.
• Score: 49.61011897610774
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating step-by-step "how-to" procedures is a key LLM capability: how-to advice is commonly requested in chatbots, and step-by-step planning is critical for reasoning over complex tasks. Yet, measuring and improving procedural validity at scale on real-world tasks remains challenging and understudied. To address this, we introduce How2Everything, a scalable framework to evaluate and improve goal-conditioned procedure generation. Our framework includes How2Mine, which mines 351K procedures from 980K web pages across 14 topics and readily scales to larger corpora. From this pool we build How2Bench, a 7K-example evaluation set balanced across topics. To reliably score model outputs, we develop How2Score, an evaluation protocol that uses an LLM judge to detect whether a generation contains any critical failure that would prevent achieving the goal. For low-cost, reproducible evaluation, we distill a frontier model into an open 8B model, achieving 80.5% agreement with human annotators. How2Bench reveals clear scaling trends across model sizes and training stages, providing signal early in pretraining. Finally, RL using How2Score as a reward improves performance on How2Bench by >10 points across three models without systematic regressions on standard benchmarks, with gains robust to superficial source-document memorization or format compliance. Taken together, How2Everything shows how pretraining web data can support a closed loop of capability evaluation and improvement at scale.

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