@YerbaShi: Can we RL coding agents without environments? The answer is yes! In Dockerless, we replace environment-based test execu…
Summary
Dockerless is a new method that enables reinforcement learning for coding agents without requiring environment setup by using an agentic verifier to explore the repository and score patches as rewards.
View Cached Full Text
Cached at: 07/05/26, 02:34 PM
Can we RL coding agents without environments?
The answer is yes! 🥳
In Dockerless, we replace environment-based test execution with an agentic verifier that explores the repo and scores patches as RL rewards.
No env-setup/test execution. But full performance! https://t.co/UzEqzAPNPs
Similar Articles
Dockerless: Environment-Free Program Verifier for Coding Agents
This paper introduces Dockerless, an environment-free agentic patch verifier that evaluates code patches without execution, outperforming existing open-source verifiers and enabling efficient post-training for coding agents.
@adithya_s_k: https://x.com/adithya_s_k/status/2067628584680710292
This article discusses how coding agents can cheat evaluations by copying known patches, and introduces Repo2RLEnv, a tool to create verifiable coding environments from real repositories to build robust benchmarks and training data for AI coding agents.
@SergioPaniego: https://x.com/SergioPaniego/status/2067270222671741360
OpenReward environments now integrate directly into TRL's GRPOTrainer via a single OpenRewardSpec, allowing zero-glue-code training against a catalog of RL environments. The integration is experimental and part of a broader effort to make environment and agent RL first-class in TRL.
@adithya_s_k: https://x.com/adithya_s_k/status/2054961319179420035
An analysis of why RL for coding tasks is gaining traction due to verifiable rewards, and why the emerging framework Harbor addresses the bottleneck of environment complexity in RL training.
@adithya_s_k: You can now train on 350+ RL Environments from OpenReward with TRL with just a few lines of code
OpenReward and TRL now support training on over 350 reinforcement learning environments with minimal code.