ProgramGym: Scaling Training Environments for Long-Horizon Program Reproduction Agents
Coding agents are increasingly used to build programs from scratch as software engineers do rather than to resolve issues within a repository or to generate a repository from a given requirements document. However, scalable verifiable training environments that force agents to discover what is required and to decide how the system should be designed remain scarce. Moreover, existing agent workflows also cannot sustain long-horizon development in which the agent explores the reference program before implementation. To address these challenges, we introduce ProgramGym, a framework consisting of Program-Env and Program-Agent for scalable training environments of long-horizon program reproduc...
| Authors | Chengyi Yang, Renxu Bao, Jue Hou et al. |
|---|---|
| Primary category | cs.SE |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
Popularity, last 2 days
0 now. Counts as reported by the source, snapshotted daily by Silytics.
Connected in the knowledge graph
- ProgramGym/programgym-1648Dataset · cited by
Models and datasets referencing this paper
- ProgramGym/programgym-1648Dataset
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.