RArao-v·Sep 22, 2026
This dashboard is almost certainly built on verl (https://github.com/verl-project/verl), which comes with a bunch of dashboarding capabilities built in (that doesn't look too dissimiliar to these dashboards).
In MiMo v2.6verl-project
verl
Python2025–2026
verl-project / verl
verl
verl/HybridFlow: A Flexible and Efficient RL Post-Training Framework
5Distinct people
6Mentions
0Explicit recommenders
24.2KGitHub stars
What to know before exploring
What it is
verl/HybridFlow: A Flexible and Efficient RL Post-Training Framework
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
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Also in these discussions
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 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 month6 dated mentions
Observed mentions: 2025-02-06 18:58:50 UTC to 2026-09-22 06:25:50 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Feb 2025 | 2025-02-01 00:00:00 UTC | 2025-03-01 00:00:00 UTC | 2 | 1 | 1 |
| Mar 2025 | 2025-03-01 00:00:00 UTC | 2025-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2025 | 2025-04-01 00:00:00 UTC | 2025-05-01 00:00:00 UTC | 1 | 1 | 0 |
| May 2025 | 2025-05-01 00:00:00 UTC | 2025-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2025 | 2025-06-01 00:00:00 UTC | 2025-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2025 | 2025-07-01 00:00:00 UTC | 2025-08-01 00:00:00 UTC | 1 | 0 | 1 |
| Aug 2025 | 2025-08-01 00:00:00 UTC | 2025-09-01 00:00:00 UTC | 1 | 0 | 1 |
| Sep 2025 | 2025-09-01 00:00:00 UTC | 2025-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2025 | 2025-10-01 00:00:00 UTC | 2025-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2025 | 2025-11-01 00:00:00 UTC | 2025-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2025 | 2025-12-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2026 | 2026-01-01 00:00:00 UTC | 2026-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2026 | 2026-02-01 00:00:00 UTC | 2026-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2026 | 2026-03-01 00:00:00 UTC | 2026-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2026 | 2026-04-01 00:00:00 UTC | 2026-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2026 | 2026-05-01 00:00:00 UTC | 2026-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2026 | 2026-06-01 00:00:00 UTC | 2026-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2026 | 2026-07-01 00:00:00 UTC | 2026-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2026 | 2026-08-01 00:00:00 UTC | 2026-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2026 | 2026-09-01 00:00:00 UTC | 2026-10-01 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
6 sourcesI trained the agent itself to write SQL, run it, check results, then rewrite until correct. The write and rewrite policies are optimized with RL, using a client–server setup in Agent Lightning and a LangGraph state machine. On a 500-sample Spider eval subset, Qwen2.5-Coder-3B with 4096 context reaches 80.4% at three turns of write and rewrite, 80.2% at one turn. After training, model Qwen2.5-Coder-1.5B can be even better than Qwen2.5-Coder-3B (untrained). I have compared multiple models and settings, hoping to shed light on tuning AI agents.
Full article: https://medium.com/@yugez/training-ai-agents-to-write-and-se...
Related projects:
- Agent Lightning as the glue: https://github.com/microsoft/agent-lightning
- verl for RL algorithms: https://github.com/volcengine/verl
- vLLM for efficient rollouts: https://github.com/vllm-project/vllm
-
Read the rest
AgentOps for collecting training data (telemetry): https://github.com/AgentOps-AI/agentops
- LangGraph for agent orchestration: https://www.langchain.com/langgraph
If you are curios, like me, how the actual reinforcement learning happens. It uses verl [1] underneath.
The paper "HybridFlow: A Flexible and Efficient RLHF Framework" [2] explains it really well.
[1] https://github.com/volcengine/verl
[2] https://arxiv.org/abs/2409.19256v2
In Show HN: Terminal-Bench-RL: Training long-horizon terminal agents with RLShow 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!
OSS reinforcement learning lib by ByteDance is used to reproduce DeepSeek R1
4 points · Open discussionThe weekly trending python project on github (https://github.com/trending/python?since=weekly) is *verl* (https://github.com/volcengine/verl)
This framework is used by Bytedance to train RL reasoning models that achieves openAI O1 level performance on olympics math problems. Code is open sourced.
Now many people are building on top of it to reproduce deepseek R1:
- https://github.com/Jiayi-Pan/TinyZero (8.6k stars): reproducing ds-r1 zero on countdown tasks for just $30, with a model with 3 billion parameters
- https://github.com/ZihanWang314/ragen (700 stars): ex-deepseek researcher use verl to build open source LLM + RL + agent model training framework
Reinforcement learning is back as the hottest topic now. Why was it not popular in previous years?
In OSS reinforcement learning lib by ByteDance is used to reproduce DeepSeek R1