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.
Request Reasoning Datasets
Chain-of-Thought Coding Traces
Stepwise code generation prompts with human-verified CoT traces, useful for reward modeling and SFT.
Request CoT Coding Traces
Multimodal Code Tasks
Applied coding problems with multimodal inputs and real-world constraints, useful for agent-based coding tasks.
Request Industry DatasetsBenchmarks 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.
Explore Benchmark
VLM-bench
Benchmark model reasoning on over 700 vision–language tasks grounded in STEM, logic, and world knowledge.
Download Report
CodeBench
900+ multilingual coding tasks with deterministic pass/fail scoring. Built for Aider compatibility, regression testing, and QA.
Request Sample DataRL 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.
Request UI Agent Environments
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.
Request Function-Calling EnvironmentsEnd-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.
Request RL EnvironmentsResearch and case studies
Ready to benchmark or debug your coding model?
Request sample data, access trajectory logs, or run a scoped SWE-Bench++ evaluation.