CoderForge-Preview: SOTA open dataset for training efficient coding agents

What this post added

This post introduces CoderForge-Preview, the largest open dataset of coding agent trajectories (258k test-verified trajectories). It details the data generation process using Qwen3-Coder-480B, including task sources (R2E-Gym, SWE-Smith, SWE-Rebench), setup with OpenHands scaffold, and filtering for test-verified trajectories. The post compares CoderForge-Preview to other datasets, highlighting its scale and long-context coverage. It also analyzes trajectory success rates, data generation costs, trajectory characteristics (length, steps), and license filtering. Furthermore, it describes the training setup for fine-tuning Qwen3-32B using sequence parallelism (Ulysses), multi-packing, and a token-level loss formulation, achieving 59.4% pass@1 on SWE-Bench Verified.