Coding datasets for post-training evaluation and agent reasoning

Reasoning-first datasets and benchmarks for function-calling, secure coding, and real-world software development.

Coding datasets

Structured prompts and real-world tasks to evaluate and improve model reasoning across software engineering workflows.

Structured Reasoning Datasets

Competitive programming tasks and rubric-aligned prompts that evaluate logic depth, planning, and correctness in code.

Chain-of-Thought Coding Traces

Stepwise code generation prompts with human-verified CoT traces, useful for reward modeling and SFT.

Multimodal Code Tasks

Applied coding problems with multimodal inputs and real-world constraints, useful for agent-based coding tasks.

Benchmarks and evaluation

Containerized benchmarks and scoring systems that test model performance in realistic development environments.

SWE-bench++

Evaluate coding agents on real GitHub tasks using containerized environments and verified trajectories.

VLM-bench

Benchmark model reasoning on over 700 vision–language tasks grounded in STEM, logic, and world knowledge.

CodeBench

900+ multilingual coding tasks with deterministic pass/fail scoring. Built for Aider compatibility, regression testing, and QA.

RL environments for coding workflows

Evaluate coding agents on real-world programming tasks, generate fine-tuning trajectories, and train reward models in reproducible, high-fidelity environments.

UI-Based RL Environments for Code Agents

Evaluate code-generation and debugging agents inside interactive IDE replicas that simulate real developer environments. These environments track edits, compile results, and run tests to measure functional accuracy.

MCP Environments for Function-Calling Agents

Train agents to call APIs, manage toolchains, and execute scripts within sandboxed development environments. Includes tool schemas, reward verifiers, and seed databases.

End-to-End Evaluation and Training Loops

Each RL environment includes prompts, verifiers, analytics harnesses, and trajectory outputs, enabling evaluation diagnostics, reward shaping, and supervised fine-tuning at scale.

Research and case studies

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Case Study\
Advancing Code-Based Physics & 2D/3D Simulation Understanding with 3,800+ Tasks\
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Created more than 3,800 simulation tasks featuring expert-authored prompts, rewrites, and structured error labels designed to surface execution, logic, and visual flaws in AI-generated physics simulations.\
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Case Study\
Creating a 1,500-Task Real-World Software Engineering Benchmark with E2E UI Test Oracles\
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Designed a large-scale, software-engineering benchmark composed of high-quality tasks drawn from a complex, open-source codebase. Each task includes a self-contained prompt from a real issue report and a solution-agnostic grader that accepts any valid solution and rejects invalid ones.\
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Case Study\
6 Unique Evaluation Frameworks: Improving LLM Coding Precision\
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Developed a detailed understanding of proprietary AI model’s strengths and weaknesses through a comprehensive evaluation approach...\
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Case Study\
Improving LLM Performance with 4,000+ Apex and SOQL Notebook Tasks\
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Created Apex and SOQL datasets to simulate real developer workflows and support next-generation assistant development. The data enables LLMs to reason through syntax errors, refactor insecure logic, and translate natural language into structured queries with precision.\
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Case Study\
Benchmarking Model Fidelity with 500 Expert-Verified Software Engineering Tasks\
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Curated 500 expert-verified tasks from real GitHub issues and pull requests (PRs). Each task includes a clear, self-contained issue report and a test designed to accept any correct solution, not only the original fix.

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Article\
Real-World Ready: Why Private Benchmarks are Essential for Trustworthy AI Code Generation\
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Public benchmarks are a start—but not enough for AGI. Learn why private benchmarks will be the path to reliable, secure, real-world AI code evaluation.\
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FAQs

What types of coding datasets does Turing provide?

Turing offers structured reasoning datasets with competitive programming tasks, human-verified chain-of-thought coding traces, and multimodal code tasks with real-world constraints for agent-based development.

What is SWE-bench++ and how does it work?

SWE-bench++ is a benchmark that evaluates coding agents on real GitHub tasks using containerized environments and verified trajectories to test performance in realistic development scenarios.

What is CodeBench used for?

CodeBench consists of 900+ multilingual coding tasks with deterministic pass/fail scoring, built for Aider compatibility, regression testing, and quality assurance.

What are RL environments for coding workflows?

RL environments are reproducible systems that let you evaluate coding agents on real-world programming tasks, generate fine-tuning trajectories, and train reward models in high-fidelity settings like IDE replicas or controlled sandboxes.

What is VLM-Bench?

VLM-Bench is Turing’s benchmark for vision-language reasoning, covering more than 700 tasks across STEM, logical inference, spatial reasoning, and real-world multimodal problem-solving.

What are UI-based RL environments for code agents?

These are interactive UI clones of development tools that simulate real developer environments where code-generation and debugging agents are evaluated by tracking edits, compile results, and test execution to measure functional accuracy.

What do MCP environments for function-calling agents include?

MCP Environments for function-calling agents include structured tool schemas, controlled execution sandboxes, verifiers, and seed tasks. They allow agents to exercise API calls, manage toolchains, and run code inside reproducible evaluation loops.

How does Turing support agentic coding workflows?

Turing supports agentic coding workflows through executable environments, benchmark-aligned datasets, and deterministic evaluation. These systems test how agents write, modify, and validate code across structured tasks, with verifiers and scoring built in.

Can Turing help improve coding performance in foundation models?

Turing improves coding performance through structured post-training systems, combining datasets, evaluation harnesses, and workflow design. These systems generate measurable signals across tasks, enabling consistent iteration on model and agent behavior.