Code Training Data for LLM Fine-Tuning and Generation

The corpus pairs 12K+ DSA problems with 64K+ solutions across 9 languages — Java, Python, C++, JavaScript, C#, PHP, and more — so the same problem can be studied in multiple languages side by side, supporting fine-tuning that targets algorithmic reasoning independent of any one language's syntax.

The coding collection extends past algorithm problems into SQL, machine coding, low-level design, and repository-history data, giving code generation and code-review models exposure to the kind of multi-file, real-world software engineering tasks that isolated coding-challenge datasets skip entirely. Machine coding and low-level design tasks in particular mirror the open-ended, multi-step engineering problems that appear in real interview and code-review workflows, rather than the single-function katas most public benchmarks rely on.

200+ legacy codebases and ~1.36B+ tokens support training on repository-level context — reading, refactoring, and reasoning across an existing codebase — rather than only generating a function from a prompt in isolation.

InfoBay's coding corpus is collected via formal enterprise agreements, not web-scraped. Problems and solutions are organized by taxonomy and reviewed rather than pulled wholesale from public repositories, which matters for both license clarity and training-signal quality.

Pre-training on scraped GitHub repositories teaches a model the statistical shape of code without a reliable way to compare correctness or style across implementations of the same task. Because InfoBay's DSA corpus is organized by problem rather than by repository, a code generation model can be shown the same task solved correctly in multiple languages and multiple valid styles, which supports both instruction tuning aimed at correctness and evaluation sets that check whether a generated solution is actually equivalent to a reference implementation — a distinction pure code-completion pre-training rarely provides on its own.