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trained my own with trl + openenv blog with open artifacts (code, models, dataset, rl env...) soon!

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Sergio Paniego

6,679 subscribers

88,850 görüntüleme • 23 gün önce •via X (Twitter)

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You can literally RL a 4B VLM to ace GeoGuesser 🌍 > Open-source code, RL environment, dataset, training setup, evals, and everything you need to reproduce it end-to-end > dropping soon!!
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You can literally RL a 4B VLM to ace GeoGuesser 🌍 > Open-source code, RL environment, dataset, training setup, evals, and everything you need to reproduce it end-to-end > dropping soon!!

Adithya S K

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Introducing the Open Deep Research app! Generate detailed reports on any topic with open source LLMs. Free & fully open source. We’re releasing everything: evaluation dataset, code, app, and blog.🔥
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Introducing the Open Deep Research app! Generate detailed reports on any topic with open source LLMs. Free & fully open source. We’re releasing everything: evaluation dataset, code, app, and blog.🔥

Together AI

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Multi-harness RL training is coming to OpenEnv > pick a model. pick a harness. pick a sandbox. train > Claude Code. Codex. Gemini CLI. OpenCode. Pi. Kimi. OpenHands. and many more. > Async RL loop. fully open-source. end-to-end, harbor compatible dropping soon 👀
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Multi-harness RL training is coming to OpenEnv > pick a model. pick a harness. pick a sandbox. train > Claude Code. Codex. Gemini CLI. OpenCode. Pi. Kimi. OpenHands. and many more. > Async RL loop. fully open-source. end-to-end, harbor compatible dropping soon 👀

Adithya S K

26,012 görüntüleme • 5 gün önce

A composite agent harness composed of system1 (multi-agent jevs) + system2 (astra) out-micros an insane computer in my WC3FT RL env (will open-source soon)
1:12

A composite agent harness composed of system1 (multi-agent jevs) + system2 (astra) out-micros an insane computer in my WC3FT RL env (will open-source soon)

Peter Wang

181,851 görüntüleme • 4 gün önce

I had 14 tabs open just to keep up with AI. arXiv, Papers With Code, every leaderboard, HuggingFace, half a dozen RL-env hubs... So I built one screen for all of it. The Bloomberg terminal for AI research. It's called Sophon 🧵
2:25

I had 14 tabs open just to keep up with AI. arXiv, Papers With Code, every leaderboard, HuggingFace, half a dozen RL-env hubs... So I built one screen for all of it. The Bloomberg terminal for AI research. It's called Sophon 🧵

serafim

48,482 görüntüleme • 3 ay önce

We used RL to train models that create curated context from long documents for downstream use by agents. The models sometimes learn to invent their own abbreviations and shorthand. Optimizing with RL for downstream use produces very different artifacts from ordinary summaries: shorter, denser, creatively concise. We call these neural cheat-sheets.
0:24

We used RL to train models that create curated context from long documents for downstream use by agents. The models sometimes learn to invent their own abbreviations and shorthand. Optimizing with RL for downstream use produces very different artifacts from ordinary summaries: shorter, denser, creatively concise. We call these neural cheat-sheets.

Applied Compute

37,265 görüntüleme • 2 ay önce

One of the first policies successfully trained with obstacles. Am I the first person to train models in RL with this many rigid bodies?
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One of the first policies successfully trained with obstacles. Am I the first person to train models in RL with this many rigid bodies?

kache

30,973 görüntüleme • 23 gün önce

we RL trained a 4B VLM to play GeoGuesser > environment, dataset, training recipe, evals, and code are all open-source. > runs on a single A100 > beat gpt 5.4 mini, haiku, qwen 3.5 122B and came close to sonnet on evals
0:20

we RL trained a 4B VLM to play GeoGuesser > environment, dataset, training recipe, evals, and code are all open-source. > runs on a single A100 > beat gpt 5.4 mini, haiku, qwen 3.5 122B and came close to sonnet on evals

Adithya S K

68,246 görüntüleme • 13 gün önce

OpenEnv has a new home: starting today, it's coordinated by a committee that includes Meta-PyTorch, Reflection, Unsloth, Modal, Prime Intellect, Nvidia, Mercor, Fleet AI, and Hugging Face frontier labs train their models and their harnesses together. Claude knows Claude Code. GPT-5.5 knows Codex. that's not an accident, it's training. open-source models deserve the same magic, but pulling that off requires infrastructure that belongs to everyone, not one lab OpenEnv is that layer. one api, any harness, any trainer, any environment rewards and training loops stay in TRL, Unsloth, wherever you already work. OpenEnv is the socket they all plug into get involved!
0:20

OpenEnv has a new home: starting today, it's coordinated by a committee that includes Meta-PyTorch, Reflection, Unsloth, Modal, Prime Intellect, Nvidia, Mercor, Fleet AI, and Hugging Face frontier labs train their models and their harnesses together. Claude knows Claude Code. GPT-5.5 knows Codex. that's not an accident, it's training. open-source models deserve the same magic, but pulling that off requires infrastructure that belongs to everyone, not one lab OpenEnv is that layer. one api, any harness, any trainer, any environment rewards and training loops stay in TRL, Unsloth, wherever you already work. OpenEnv is the socket they all plug into get involved!

Sergio Paniego

32,085 görüntüleme • 3 ay önce

Video understanding isn't just recognizing —it demands reasoning across thousands of frames. Meet Long-RL🚀 Highlights: 🧠 Dataset: LongVideo-Reason — 52K QAs with reasoning. ⚡ System: MR-SP - 2.1× faster RL for long videos. 📈 Scalability: Hour-long videos (3,600 frames) RL on a single node (8×A100s). 🖼️📝🎵 RL training for video, text, audio — works with VILA, Qwen series, and image/video generation models 🎨🎬 📄 Paper: 🎥 Demo: 💻 Code:
1:19

Video understanding isn't just recognizing —it demands reasoning across thousands of frames. Meet Long-RL🚀 Highlights: 🧠 Dataset: LongVideo-Reason — 52K QAs with reasoning. ⚡ System: MR-SP - 2.1× faster RL for long videos. 📈 Scalability: Hour-long videos (3,600 frames) RL on a single node (8×A100s). 🖼️📝🎵 RL training for video, text, audio — works with VILA, Qwen series, and image/video generation models 🎨🎬 📄 Paper: 🎥 Demo: 💻 Code:

Yukang Chen

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train YOLOv9 on your dataset tutorial - run inference with a pre-trained COCO model - fine-tune model on custom dataset - evaluate the trained model - run inference with a fine-tuned model blogpost: ↓ read more
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train YOLOv9 on your dataset tutorial - run inference with a pre-trained COCO model - fine-tune model on custom dataset - evaluate the trained model - run inference with a fine-tuned model blogpost: ↓ read more

SkalskiP

111,876 görüntüleme • 2 yıl önce

None of the video gen models do a real CRT terminal animation look. So I trained my own with fal. CRT-anim : open-source LoRA for LTX-2.3 22B. Runs on fal + ComfyUI.
1:04

None of the video gen models do a real CRT terminal animation look. So I trained my own with fal. CRT-anim : open-source LoRA for LTX-2.3 22B. Runs on fal + ComfyUI.

Lovis Odin

41,911 görüntüleme • 5 ay önce

📈 Scale open models with Hugging Face Text Generation Inference! 🚀 Customize with LoRA adapters ⚙️ Optimize with PEFT and TRL ☁️ Deploy on Google Cloud Platform with ease 🤖 Watch the demo from #GemmaDeveloperDay Tokyo →
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📈 Scale open models with Hugging Face Text Generation Inference! 🚀 Customize with LoRA adapters ⚙️ Optimize with PEFT and TRL ☁️ Deploy on Google Cloud Platform with ease 🤖 Watch the demo from #GemmaDeveloperDay Tokyo →

Google for Developers

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We just released the world's first open-source humanoid physical strike AI! The AI is trained with large-scale self-play RL with a pre-trained ASE model from Jason Peng. Thank ZiyiBird for this amazing work.
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We just released the world's first open-source humanoid physical strike AI! The AI is trained with large-scale self-play RL with a pre-trained ASE model from Jason Peng. Thank ZiyiBird for this amazing work.

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Today we presented our work on Action Recognition with 3D pose and tracking at #CVPR. We also released our code, models, and dataset of 1.5 million human trajectories. Code: Project Page:
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Today we presented our work on Action Recognition with 3D pose and tracking at #CVPR. We also released our code, models, and dataset of 1.5 million human trajectories. Code: Project Page:

Jathushan Rajasegaran

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Run Cline on Local AI models with Atomic Chat! Cline is a coding agent trusted by 8M+ developers. Write, refactor, ship code securely on your own hardware with local models powered by atomic.chat — no cloud, private, free and open-source
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Run Cline on Local AI models with Atomic Chat! Cline is a coding agent trusted by 8M+ developers. Write, refactor, ship code securely on your own hardware with local models powered by atomic.chat — no cloud, private, free and open-source

atomic.chat

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Advice for AI engineers 💡 Browser control is possible with a Small Model, like LFM2-350M by Liquid AI . Here's a 60-minute deep dive on how to fine-tune with RL and OpenEnv by Hugging Face Enjoy ↓
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Advice for AI engineers 💡 Browser control is possible with a Small Model, like LFM2-350M by Liquid AI . Here's a 60-minute deep dive on how to fine-tune with RL and OpenEnv by Hugging Face Enjoy ↓

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Introducing Open Deep Research! A fully open-source Deep Research tool that: • writes comprehensive reports • does multi-hop search and reasoning • generates cover images & pod-casts! We’re releasing everything: evaluation dataset, code and blog.🔥 Example output report👇
0:34

Introducing Open Deep Research! A fully open-source Deep Research tool that: • writes comprehensive reports • does multi-hop search and reasoning • generates cover images & pod-casts! We’re releasing everything: evaluation dataset, code and blog.🔥 Example output report👇

Together AI

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