Training Data for Reasoning Models and Chain-of-Thought SFT
Reasoning models need examples that show structured problem solving, not only final answers. InfoBay supports chain-of-thought SFT and reasoning-model training with STEM textbooks, explanation-rich Q&A, and cross-language coding datasets.
The problem
Reasoning models trained only on final-answer datasets learn to mimic conclusions without the intermediate steps that make those conclusions reliable on unseen problems. Supervised fine-tuning that skips the reasoning chain teaches a model to sound confident, not to reason correctly — a gap that widens on multi-step math, diagnostic, and algorithmic tasks where the answer is only as good as the path that produced it. InfoBay's textbook, Q&A, and coding corpora are built around explicit reasoning structure rather than isolated answers, giving reasoning-model teams the chain-of-thought density that generic instruction data can't supply.
Corpus assets & provenance
STEM textbooks as structured reasoning chains
+InfoBay's textbook corpus spans 42K+ ISBN-attributed books and 2.8B+ words across 15 languages and 5K+ subjects, drawn from mathematics, medical science, engineering, and applied STEM domains. Because each book carries a step-by-step derivation rather than a bare answer key, the corpus is a natural source of chain-of-thought supervision — the kind FineWeb-Edu (NeurIPS 2024) showed lifts ARC by +24% and MMLU by +12% over unfiltered web text. InfoBay's textbooks are educational by origin, not by post-hoc filtering, so the reasoning density is present from the first token rather than engineered in through aggressive dataset pruning.
Q&A data with explicit reasoning traces
+Reasoning improves when a model sees the steps a domain expert takes to reach an answer, not just the final label. InfoBay's Q&A corpus (6.7M+ human-verified pairs) is built with explicit reasoning traces authored by domain subject-matter experts rather than commodity crowdworkers, covering STEM and non-STEM domains with equations, options, and worked explanations that mirror the intermediate steps a reasoning model needs to imitate during SFT.
Algorithmic depth from the coding corpus
+Reasoning benchmarks reward more than natural-language logic — algorithmic decomposition, complexity analysis, and edge-case handling are reasoning skills in their own right. InfoBay's coding corpus includes 12K+ DSA problems and 64K+ solutions across 9 languages (Java, Python, C++, JavaScript, C#, PHP, and more), plus 200+ legacy codebases and ~1.36B+ tokens, organized by problem so the same task can be studied in parallel across languages rather than as one-off, language-specific examples.
Provenance that survives model-risk review
+InfoBay's corpus is collected via formal enterprise agreements, not web-scraped. Every textbook carries ISBN-level attribution, giving reasoning-model teams a provenance chain that supports review under EU AI Act Article 10, India DPDP Act, CCPA, with GDPR-eligible lineage documented for legal and model-risk teams before data enters a training run.
Specifications
| Corpus | Format | Volume | Language coverage | Provenance |
|---|---|---|---|---|
| Textbooks | JSONL (gzip) + Parquet metadata manifest | 2.8B+ words / 42K+ books | 15 languages | ISBN-attributed, licensed |
| Q&A | JSONL, structured explanation fields | 6.7M+ pairs | Multilingual, STEM + non-STEM | SME-authored reasoning traces |
| Coding | JSONL per language + problem taxonomy JSON | ~1.36B+ tokens / 64K+ solutions | 9 languages | Curated, not GitHub-scraped |
Measurable impact
Measurable impact on reasoning evals
Every reasoning-model engagement is scoped against target evaluations — MMLU, ARC, GPQA Diamond, GAIA — with FineWeb-Edu (NeurIPS 2024)'s published result (educational text: +24% ARC, +12% MMLU over web text) used as the external reference point for what source quality alone can move. Early InfoBay-scoped reasoning engagements have measured gains of up to +27% on ARC and +15% on MMLU over each team's pre-engagement baseline, measured against the same agreed evaluation set before and after fine-tuning.
How an engagement works
- 1
Baseline your reasoning gaps
Every engagement starts by running your model against the target evals — MMLU, ARC, GPQA Diamond, GAIA, or your own eval set — to identify where reasoning breaks down before any data is scoped.
- 2
Scope textbook, Q&A, and coding mix
Subject, language, and difficulty coverage are scoped against the failure modes the baseline surfaced, drawing from STEM textbooks, SME-authored Q&A, and the DSA coding corpus in whatever ratio matches your training stage.
- 3
Deliver and re-measure
Data ships with ISBN and SME-authorship metadata attached; the same eval suite is re-run after fine-tuning so the engagement is measured in benchmark deltas, not delivered volume.
Answers for buyers
FAQ
What data actually improves chain-of-thought reasoning benchmarks?+
STEM textbooks with worked derivations, SME-authored Q&A with explicit reasoning traces, and a DSA-focused coding corpus that adds algorithmic decomposition — not just more instruction-tuning volume.
Is the textbook corpus scraped from the web?+
No. It is 42K+ ISBN-attributed, licensed textbooks across 15 languages and 5K+ subjects — educational by source, not by filtering a web crawl.
Who writes the reasoning traces in the Q&A corpus?+
Domain subject-matter experts, not crowdworkers — the corpus is built specifically to capture the intermediate steps an expert takes to reach an answer.
Does the coding corpus support cross-language reasoning training?+
Yes. Problems are organized so the same DSA task is available across 9 languages (Java, Python, C++, JavaScript, C#, PHP, and more), which supports training on algorithmic reasoning independent of syntax.
Which benchmarks do engagements target?+
MMLU, ARC, GPQA Diamond, GAIA are the standard target evals; each engagement is measured against a baseline you agree on before data is scoped.
Can this data be used alongside our own proprietary reasoning data?+
Yes. InfoBay's textbook, Q&A, and coding corpora are typically blended with proprietary data rather than replacing it, scoped to the specific reasoning gaps a baseline eval identifies.
Next steps