How Cosine’s model was trained
Cosine’s proprietary Genie model is purpose-built for software engineering — trained on open-source code and refined with reinforcement learning for autonomy, reasoning, and code correctness.
Cosine’s Genie model is purpose-built for software engineering, optimized for autonomy, reasoning, and code correctness.
Unlike general-purpose LLMs, Genie was trained to understand real-world repository structures, dependency graphs, and test-driven workflows.
Training sources and approach
Pretraining: On high-quality, permissively licensed open-source repositories (e.g., MIT, Apache, BSD).
Filtering: Removal of PII, insecure code, and non-source text.
Domain diversity: Data across 20+ languages and frameworks (Python, Java, JS/TS, C#, Go, etc.).
Reinforcement learning for engineering tasks
Genie is post-trained with reinforcement signals specific to engineering quality:
Successful vs. failed task completions.
Code compile/test outcomes.
PR merge acceptance rates.
Efficiency of fixes and refactors.
This reinforcement phase teaches Genie to plan, validate, and reason about software — not just autocomplete text.
Continuous evaluation and fine-tuning
Cosine runs continuous regression tests on real repositories to measure:\n- Code accuracy and runtime stability. \n- Test pass rates and diff efficiency. \n- Hallucination and error frequency. \n\nEnterprise deployments may use private fine-tuning on internal codebases, fully contained within their VPC or on-prem environments — no data egress.
Model safety and data governance
Zero customer data used for training.
PII and license filtering applied pre-training.
Model cards document dataset sources, evaluation benchmarks, and update history.
Aligned with NIST AI RMF and EU AI Act governance frameworks.
Why this matters
This purpose-built training pipeline makes Genie more reliable for real engineering tasks — from legacy refactors to multi-service migrations — and ensures Cosine is trustworthy, secure, and audit-ready for enterprise use.
Related pages
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