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OpenPipe
ART
Python2025–2026
OpenPipe / ART

ART

Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training. Reinforcement learning for Qwen3.6, GPT-OSS, Llama, and more!

AI & Machine LearningLearning & Reference
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4Distinct people
5Mentions
0Explicit recommenders
10.9KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training. Reinforcement learning for Qwen3.6, GPT-OSS, Llama, and more!

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month5 dated mentions

Observed mentions: 2025-04-30 15:35:00 UTC to 2026-02-23 06:54:26 UTC.

0122025-04-01 00:00:00 UTC to 2025-05-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsApr 20252025-05-01 00:00:00 UTC to 2025-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-06-01 00:00:00 UTC to 2025-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-07-01 00:00:00 UTC to 2025-08-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2025-08-01 00:00:00 UTC to 2025-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-01 00:00:00 UTC to 2025-10-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 commentsSep 20252025-10-01 00:00:00 UTC to 2025-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-11-01 00:00:00 UTC to 2025-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-12-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-01-01 00:00:00 UTC to 2026-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-02-01 00:00:00 UTC to 2026-03-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsFeb 2026

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Apr 20252025-04-01 00:00:00 UTC2025-05-01 00:00:00 UTC110
May 20252025-05-01 00:00:00 UTC2025-06-01 00:00:00 UTC000
Jun 20252025-06-01 00:00:00 UTC2025-07-01 00:00:00 UTC000
Jul 20252025-07-01 00:00:00 UTC2025-08-01 00:00:00 UTC110
Aug 20252025-08-01 00:00:00 UTC2025-09-01 00:00:00 UTC000
Sep 20252025-09-01 00:00:00 UTC2025-10-01 00:00:00 UTC202
Oct 20252025-10-01 00:00:00 UTC2025-11-01 00:00:00 UTC000
Nov 20252025-11-01 00:00:00 UTC2025-12-01 00:00:00 UTC000
Dec 20252025-12-01 00:00:00 UTC2026-01-01 00:00:00 UTC000
Jan 20262026-01-01 00:00:00 UTC2026-02-01 00:00:00 UTC000
Feb 20262026-02-01 00:00:00 UTC2026-03-01 00:00:00 UTC101

What Hacker News says

5 sources
iFire·Feb 23, 2026
Huh? I thought the latest advance in computing (spring 2025 - last year) is self-play / reinforcement learning. Like we've ran out of training data a few years ago. https://github.com/OpenPipe/ART Reinforcement learning having the large language model devise puzzles that they solve via llm-as-judge. The definition of llm-as-judge is your llm generate 8-12 trajectories and a different llm judges the result. I'd use an oracle like windows or linux operating system execution for the problem of ISA-assembly creation. The winning entries are used to train the large language model.
In Code is not being read anymore, right?
kcorbitt·Jul 11, 2025Submission

Show HN: RULER – Easily apply RL to any agent

Hey HN, Kyle here, one of the co-founders of OpenPipe. Reinforcement learning is one of the best techniques for making agents more reliable, and has been widely adopted by frontier labs. However, adoption in the outside community has been slow because it's so hard to implement. One of the biggest challenges when adapting RL to a new task is the need for a task-specific "reward function" (way of measuring success). This is often difficult to define, and requires either high-quality labeled data and/or significant domain expertise to generate. RULER is a drop-in reward function that works across different tasks without any of that complexity. It works by showing N trajectories to an LLM judge and asking it to rank them relative to each other. This sidesteps the calibration issues that plague most LLM-as-judge approaches. Combined with GRPO
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(which only cares about relative scores within groups), it just works (surprisingly well!). We have a full writeup on the blog, including results on 4 production tasks. On all 4 tasks, small Qwen 2.5 models trained with RULER+GRPO beat the best prompted frontier model, despite being significantly smaller and cheaper to run. Surprisingly, they even beat models trained with hand-crafted reward functions on 3/4 tasks! https://openpipe.ai/blog/ruler Repo: https://github.com/OpenPipe/ART
81 points · Open discussion
kcorbitt·Apr 30, 2025Submission

Show HN: ART – a new open-source RL framework for training agents

Hey HN, I wanted to share a new project we've been working on for the last couple of months called ART (https://github.com/OpenPipe/ART). ART is a new open-source framework for training agents using reinforcement learning (RL). RL allows you to train an agent to perform better at any task whose outcome can be measured and quantified. There are many excellent projects focused on training LLMs with RL, such as GRPOTrainer (https://huggingface.co/docs/trl/main/en/grpo_trainer) and verl (https://github.com/volcengine/verl). We've used these frameworks extensively for customer-facing projects at OpenPipe, but grew frustrated with some key limitations: - Multi-turn workflows, where the agent calls a tool, gets a response, and calls another, are not well supported. This makes them a non-starter for any task that requires an agent to perform a se
Read the rest
quence of actions. - Other frameworks typically have low GPU efficiency. They may require multiple H100 GPUs just to train a small 7B parameter model, and aren't able to keep the GPUs busy consistently during both the "rollout" and "training" phases of the training loop. - Existing frameworks are typically not a convenient shape for integrating with existing agentic codebases. Existing trainers expect you to call raw text completion endpoints, and don't automatically provide industry-standard chat completion APIs. ART is designed to address these limitations and make it easy to train high-quality agents. We've also shared many details and practical lessons learned is in this post, which walks through a demo of training an email research agent that outperforms o3 (https://openpipe.ai/blog/art-e-mail-agent). You can also find out more about ART's architecture in our announcement post (https://openpipe.ai/blog/art-trainer-a-new-rl-trainer-for-ag...). Happy to answer any questions you have!
116 points · Open discussion

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