# CodeMidas: Scaling Agentic Coding RL Environments from Code Itself

Source: [arXiv](https://arxiv.org/abs/2609.22068v1)  
Feed7 permalink: https://feed7.dev/p/2609-22068v1-0mwdzky  
Published: 2026-09-18T17:55:17.000Z  
Trust: Needs Review (needs_review)

## Why Included

CodeMidas derives verified coding-agent RL tasks directly from working source, widening training data beyond issues and commits while improving repair, construction, and terminal benchmarks.

## Source Summary

CodeMidas turns existing implementations into executable RL environments using source code as the only task-specific input. Its agents derive specifications, build execution-grounded tests, and filter tasks through checks and repeated solution attempts, producing **5,545 tasks from 3,185 repositories**.

## Practical Implication

For coding-agent training, treat working code as a source of behavioral tasks even when issue and commit histories are sparse. MiMo-V2.5 trained with GRPO improved across all five tested benchmarks, including **+17% on ProgramBench** and **+11.7% on DeepSWE**.

## Agent-Ready Context

CodeMidas turns existing implementations into executable RL environments using source code as the only task-specific input. Its agents derive specifications, build execution-grounded tests, and filter tasks through checks and repeated solution attempts, producing **5,545 tasks from 3,185 repositories**.

For coding-agent training, treat working code as a source of behavioral tasks even when issue and commit histories are sparse. MiMo-V2.5 trained with GRPO improved across all five tested benchmarks, including **+17% on ProgramBench** and **+11.7% on DeepSWE**.

The abstract reports that more high-quality tasks helped and that trained agents explored and self-verified more, but it does not establish how well the pipeline handles underspecified, unsafe, or hard-to-execute repositories.

## Connected Context

Feed7 judgment across 831 accumulated Signals:

CodeMidas broadens executable task generation beyond repositories with usable issue or PR histories by deriving behavior from implementations themselves, and supplies training gains rather than only a data-pipeline proposal. It complements history-derived environments, while leaving task validity, unsafe repositories, execution feasibility, and supervision quality as unresolved filters rather than solved properties.

- [Change2Task: From Repository Changes to Executable Coding Agent Tasks and Environments](https://feed7.dev/p/2607-28591v1-169rlqh) — Change2Task derives executable tasks from merged changes, whereas CodeMidas uses implementations alone, making the approaches complementary for repositories with rich versus sparse histories.
- [MindForge: Teaching Small Language Models Whole-Life-Cycle Software Engineering via Source-Free Program Synthesis](https://feed7.dev/p/2607-27146v1-0gcx6ia) — Both report ProgramBench gains from constructed coding environments, but MindForge reconstructs programs from documentation and behavior while CodeMidas derives tasks from existing source.
- [SWE-Prime: Fewer Trajectories, Better Performance](https://feed7.dev/p/2608-27449v1-0lkylqd) — SWE-Prime shows that successful trajectories still require quality filtering, an implementation consequence for CodeMidas after its task filters and repeated solution attempts produce training traces.
- [State of Data — Sean Cai, Independent / State of Data](https://feed7.dev/p/state-of-data-sean-cai-independent-state-of-data-0v9fy69) — The real-trace thesis provides a useful contrast: CodeMidas demonstrates gains from manufactured source-derived tasks, but does not establish that they capture the behavior or scaffold dependence of live work.

## Context Map

- Layer: benchmark
- Domains: coding
- Topics: coding-agents, agent-evals, harness-engineering

## Uncertainty

- The abstract reports that more high-quality tasks helped and that trained agents explored and self-verified more, but it does not establish how well the pipeline handles underspecified, unsafe, or hard-to-execute repositories.

## Agent Instruction

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