👋Hy4 is here, new with vision.
Firmly among the leading open models.
More productive everywhere: finance, frontend, game, officework, research…
Built responsibly, with cyber/biosecurity in mind.
Try it out and let us know!
🔗：hy.tencent.ai/research/hy4?l…View quoted post on X
RLVR has become the recipe for agentic post-training. But for Computer-Use Agents, the bottleneck is not the algorithm, it is the data. 🐌
🚀 We introduce CUA-Gym: a scalable, lightweight synthesis engine that turns arbitrary task queries into verifiable RLVR data for computer-use agents. The largest open CUA RLVR dataset to date:
🎯 32,122 verifiable RLVR tasks with programmatic setup scripts + rewards
🌐 110 environments: 16 desktop apps + 94 synthesized mock web apps
🏆 Qwen3.5-based CUA models trained with GSPO reach 72.6% on OSWorld-Verified and 56.6% on WebArena
📄 Paper: huggingface.co/papers/2605.25…
🏠 Homepage: cua-gym.xlang.ai
🤗 Dataset: huggingface.co/datasets/xlang…
💻 Codebase: github.com/xlang-ai/CUA-G…
🧩 Environments: github.com/xlang-ai/CUA-G…
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Kimi K2.5 tech report just dropped!
Quick hits:
- Joint text–vision training: pretrained with 15T vision-text tokens, zero-vision SFT (text-only) to activate visual reasoning
- Agent Swarm + PARL: dynamically orchestrated parallel sub-agents, up to 4.5× lower latency, 78.4% on BrowseComp
- MoonViT-3D: a unified image–video encoder with 4× temporal compression, enabling 4× longer videos in the same context
- Toggle: token-efficient RL, 25–30% fewer tokens with no accuracy drop
Here's our work toward scalable, real-world agentic intelligence. More details in the report 👉github.com/MoonshotAI/Kim…View quoted post on X
🥝Meet Kimi K2.5, Open-Source Visual Agentic Intelligence.
🔹Global SOTA on Agentic Benchmarks: HLE full set (50.2%), BrowseComp (74.9%)
🔹Open-source SOTA on Vision and Coding: MMMU Pro (78.5%), VideoMMMU (86.6%), SWE-bench Verified (76.8%)
🔹Code with Taste: turn chats, images & videos into aesthetic websites with expressive motion.
🔹Agent Swarm (Beta): self-directed agents working in parallel, at scale. Up to 100 sub-agents, 1,500 tool calls, 4.5× faster compared with single-agent setup.
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🥝K2.5 is now live on kimi.com in chat mode and agent mode.
🥝K2.5 Agent Swarm in beta for high-tier users.
🥝For production-grade coding, you can pair K2.5 with Kimi Code: kimi.com/code
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🔗 API: platform.moonshot.ai
🔗 Tech blog: kimi.com/blogs/kimi-k2-…
🔗 Weights & code: huggingface.co/moonshotai/Kim…View quoted post on X
Pretraining has scaling laws to guide compute allocation. But for RL on LLMs, we lack a practical guide on how to spend compute wisely.
We show the optimal compute allocation in LLM RL scales predictably.
↓ Key takeaways belowView quoted post on X
