Framework to mine and evaluate how-to procedures for improving LLM reasoning capabilities.
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.
LLMs generate plausible but sometimes incomplete procedures, missing critical steps that break real-world usability.
How2Everything framework extracts 351,000 validated procedures from the web across fourteen diverse topic categories.
How2Score evaluation protocol uses human annotators to identify critical failures that prevent procedural goals from being achieved.
Initial inter-annotator agreement was very low (Krippendorff's alpha 0.273), making critical failure definition challenging.
Existing evaluation methods like BLEU scores and perplexity fail to capture whether procedures actually work in practice.
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