# 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 \ Case Study\ Advancing Code-Based Physics & 2D/3D Simulation Understanding with 3,800+ Tasks\ \ 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.\ \ Read Case Study \ Case Study\ Creating a 1,500-Task Real-World Software Engineering Benchmark with E2E UI Test Oracles\ \ 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.\ \ Read Case Study \ Case Study\ 6 Unique Evaluation Frameworks: Improving LLM Coding Precision\ \ Developed a detailed understanding of proprietary AI model’s strengths and weaknesses through a comprehensive evaluation approach...\ \ Read Case Study \ Case Study\ Improving LLM Performance with 4,000+ Apex and SOQL Notebook Tasks\ \ 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.\ \ Read Case Study \ Case Study\ Benchmarking Model Fidelity with 500 Expert-Verified Software Engineering Tasks\ \ 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. \ Article\ Real-World Ready: Why Private Benchmarks are Essential for Trustworthy AI Code Generation\ \ 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.\ \ Read Article ## 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.