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Explore the articlesCoding & SWE · Terminal · Multimodal · Synthetic Data
Why can a coding patch pass tests and still be wrong? A closer look at task specifications, verifier errors, reward reliability, and the limits of executable feedback in SWE RL.
Read articleExplore coding and software engineering training data through repository tasks, code context, edit trajectories, and test feedback, with methods and verification boundaries.
Explore terminal agent training data through actions, observations, environment state, and execution trajectories, with artifact checks and recovery conditions.
Explore multimodal post-training data across images, audio, video, and text, with instruction design, temporal and spatial evidence, and semantic verification.
Explore frontier research, open-source projects, and methods in data synthesis. Analyze approaches, quality boundaries, and training value across instructions, tasks, trajectories, and preference data.
Data quality starts with a verifiable process.
239.AI explores coding tasks, terminal trajectories, multimodal samples, and synthetic data, including frontier research and open-source projects. Technical insights and method analysis help model and agent teams assess data for their training goals.
A dataset's value depends on how its content fits the training goal. Task coverage, sample structure, difficulty, and correctness are key to assessing suitability.
Discuss dataset purchases or request custom data. Start with your training use case and the samples you need, then work through data scope, volume, budget, and delivery timing.
Explore dataset content, quality, and limitations in the articles, then discuss the data you need. Purchase terms, delivery arrangements, and pricing are agreed for each engagement.
