wang

[wang @weixunwang]

@LeYangco @ProfYanJunchi Special thanks to Yang for the excellent work! Your contributions have been invaluable and greatly appreciated. Well done! 👏

[Yang Li @ ICML @LeYangco]

We would like to express our gratitude to RethinkLab of Shanghai Jiao Tong University and the Roll Team at Alibaba ATH Group for their support. Special thanks to @ProfYanJunchi and @weixunwang for their supervision and discussion😃😃

[wang @weixunwang]

@ethantsliu @oneill_c I can't find the SisyphusRL library on GitHub.

[Shamane Siri | Pluralis @GShamane]

Agentic RL environments are becoming critical. We integrated OpenReward (https://t.co/PGK3Ilhj1C) into Alibaba’s ROLE (https://t.co/3SkXlH5hRZ).

Details: https://t.co/BWR9lZ0dcJ

[wang @weixunwang]

@GShamane @rosstaylor90 thx！

[wang @weixunwang]

@novasarc01 Part II: ROLL Flash -- Accelerating RLVR and Agentic Training with Asynchrony https://t.co/9ui6wheb3U

[wang @weixunwang]

@LeYangco ？？现在再转🥷

[wang @weixunwang]

@Scott3131493885 🥷🥷🥷

[Scott @Scott3131493885]

Are you also struggling with RL on long-horizon, high-difficulty agentic tasks, especially when positive rewards are sparse? Check out the latest blog from the ROLL team: https://t.co/w1hRdX7cX6

[wang @weixunwang]

@AISafetyMemes 🥷🥷🥷

[wang @weixunwang]

@AlexanderLong 🥷🥷

[wang @weixunwang]

@JoshKale 🥷🥷

[wang @weixunwang]

@helansydney My team and I hit some challenges doing RL training in terminal environments, so we wrote a blog sharing what we learned. We opened with two memes: one about researchers jumping from RLVR to Agentic RL, and another showing the chaos when RL training fails and no one knows why.

[wang @weixunwang]

@helansydney My team and I hit some challenges doing RL training in terminal environments, so we wrote a blog sharing what we learned. We opened with two memes: one about researchers jumping from RLVR to Agentic RL, and another showing the chaos when RL training fails and no one knows why.

[Sydney He @helansydney]

The Bitter Lesson Behind Building Agentic RL in Terminal Environments

This blog post summarizes our practical experience over the past three months working on Agentic RL.

For more details, please refer to: https://t.co/G1OgSlnnwy #LLM #RL #Agent #AgenticRL

[wang @weixunwang]

I'm claiming my AI agent "weixun_rl-bot" on @moltbook 🦞

Verification: cave-V8VT

[FutureLivingLab @FutureLab2025]

Loved this breakdown — thanks for taking the time

It really does feel like a big step forward for open-source agentic training infrastructure!

Introducing ALE — a full-stack Agentic Learning Ecosystem that closes the loop from
execution → feedback → learning.

Three components power this loop:
• ROCK runs large-scale sandboxed execution to gather reliable trajectories.
• ROLL scales post-training with asynchronous rollouts and RL optimization.
• iFlow CLI keeps training and deployment workflows consistent end to end.

Built on ALE, we also release ROME — a production-ready agentic model trained on 1M+ real trajectories.

With its low barrier to serving a 30B model, you can build your own “super ROME” — drop your ideas, thoughts or usage feedback below

For more updates, follow us @FutureLab2025

[wang @weixunwang]

@godofprompt 🥷

[wang @weixunwang]

@rosinality 🥷

[Yang Li @ ICML @LeYangco]

Check out our new work: “Let It Flow: Agentic Crafting on Rock and Roll” — introducing ALE, an open Agentic Learning Ecosystem with ROLL, ROCK, and iFlow CLI to streamline Agent LLM development from training to deployment, plus ROME, a production-ready agentic model trained on 1M+ real trajectories using our novel IPA algorithm that optimizes credit assignment at the semantic interaction level. Built for the community, battle-tested in practice! 🔗 https://t.co/oTwn0jXBG0