UC Berkeley Sky

RT @aaron_jli: Do you know that past experience can substantially reduce the token cost of your coding agent? Introducing our recent work TokenEconomizer: 18.4–30.9% lower cache-aware cost on SWE-Bench, with comparable task success. We deployed it as a plugin for mainstream coding agents - feel free to try it out and let us know your feedback! Plugin: Paper: Project page: (1/3)

RT @lianapatel_: Introducing ✨Token-Gated Language Models✨ for up to 11x faster prefill by learning to skim context, conditionally forwarding only tokens that matter. Akin to the shift from dense models to MoEs, the next frontier of efficient LLMs is conditional computation over a new scaling axis: context tokens🧵👇

RT @MayankMish98: Releasing FLAT: Flash Linear Attention on TPUs 🚀 Pallas training kernels for Linear Attention, Mamba-2 and GLA. On one TPU v6e, forward + backward is up to 3x faster than FLA on an H100 for GLA, and 1.5x faster for Mamba-2. 🧵

RT @erictang000: Excited to announce the release of SkyRL v0.4.0! Some highlights: - Support for GLM 5.3 Flash, GLM 5.3, and Kimi K2.7 - Top-P sampler replay and optimized R3 data transfer - Full FP8/MXFP8 RL - Sharded P2P weight transfer Blog: Details in 🧵

RT @Reveur_7: What happens if you run @gepa_ai style evolutionary search on @NVIDIAAI's Graph-as-Policies? Do the robot policies actually get any better? My latest blogpost explores this:

RT @melissapan: Does your Claude model really need Claude Code…? 🤔 We evaluate 7 models on Claude Code, Codex, and Pi. Three surprising findings emerge: 1️⃣Harness choice has little effect on task success rate, but can significantly affect the cost 2️⃣A simple harness can be competitive 3️⃣The native harness isn’t always the best. Millions of people are using coding agents, but the impact of harness choice remains unclear. (1/n) More details in the thread. 🧵

RT @shulynnliu: Agents let us build systems for different workloads and requirements. But… can we trust what they build? We release 🌟SkySynth🌟: an engine for synthesizing high-performance, just-in-time (JIT) systems we can trust, by co-evolving formal proofs and tests alongside the code. Results: 💿 KV stores up to 2.3× faster than Redis and FASTER + formally verified stores with 2.9× Claude Code's pass rate 🚏 Model routers up to 48% cheaper than a general router ⚡ Specialized inference engine with 2.2× the throughput of vLLM/SGLang 🧵👇

RT @zongheng_yang: Today, SkyPilot is out of stealth. Building custom intelligence is now existential. We help frontier AI teams build intelligence faster by removing their biggest bottleneck: AI compute fragmentation. Frontier teams like @appliedcompute, @AbridgeHQ, @hippocraticai, @hcompany_ai, and @nubank already run on SkyPilot, with 10x faster time-to-intelligence and double-digit increase in GPU utilization. AI teams today get compute anywhere they can. They then firefight compute fragmentation across providers. Researchers burn time on workload setup. Infra gets paged when GPUs go down. Frontier teams build slowly even on the fastest compute. @skypilot_org turns your fragmented compute into one AI supercomputer, so you run frontier workloads faster. Many users manage 10,000+ GPUs across providers with SkyPilot. GPU hours consumption has grown 6x in the last 6 months. 1/ We're launching SkyPilot Platform — the AI compute platform for frontier AI teams to manage large GPU fleets and accelerate building custom intelligence. Optimized for fleet management, team governance, and frontier workloads — pretraining, post-training, multi-cluster serving, and sandboxes. SkyPilot open source users can switch to the platform with a server URL change. 2/ We've raised over $20M led by @Lux_Capital (@breeves08), with participation from @AmplifyPartners (@dauber, @lennypruss), @coatuemgmt, @FoundationCap (@ashugarg , @JayaGup10), @RaceCapital, @thehousefund, and top operators like @alighodsi (CEO, Databricks), @JeffDean (Chief Scientist, Google), @rauchg (CEO, Vercel), @amasad (CEO, Replit), @ClemDelangue (CEO, @huggingface) and more. We're hiring across Engineering and GTM to deliver the platform for the next decade of AI. Above all, I'm excited to be building with the incredible team we've assembled, along with my cofounders Zhanghao @Michaelvll1, Romil @bromil101, Scott, and Ion @istoica05. If you firefight AI compute, let's build.

RT @RichardZ412: How can we train small agentic models that are highly capable of terminal use and coding? Announcing OpenThoughts-Agent + OpenThinkerAgent-32B, the strongest Qwen-3 based open-data agentic model: 44.8% avg across 7 agentic benchmarks! (1/n)

RT @Reveur_7: What if a robot policy weren't a neural net or a test-time chat loop, but a multi-file code repo selected from a Pareto frontier of genetically evolved candidates? RHO moves all its LLM exploration to training time, then runs that repo on scenes it was never trained on. 🧵👇🏽

RT @zwcolin: 👀Humans compare images by looking back and forth. Many open-weight VLMs encode each image independently, and defer comparison to the LM. We introduce SVE: Stateful Visual Encoders for Vision-Language Models, where the visual encoder itself becomes change-aware. 🌐Project: 📰Paper: 💻Code: 1/n

RT @yangzhen04: Static benchmarks are dying — they tend to get saturated quickly. Evaluation and training data should co-evolve with frontier models. We released BenchEvolver — a framework that automatically evolves saturated problems into harder, verified tasks for evaluating frontier models, which can also serve as useful self-improvement signals for RL. New work from UC Berkeley @berkeley_ai @BerkeleyRDI @BerkeleySky Project Page: Paper:

RT @corban_villa: Agents are finding more vulnerabilities than ever. But it turns out there are gaps in existing vulnerability discovery. Over the past 90 days vs. a year ago, web vulnerabilities (XSS/SQLi/CSRF) are down 66% and memory safety exploitability is down 3.5x. We built the Agentic Vulnerability Coverage Map to track it all, updated daily. Introducing the Berkeley Vulnerability Initiative: ⤵️

RT @mirmiroyan: We release Recon — a new approach to reasoning synthesis for user modeling. The key insight: post-hoc rationalization ≠ reasoning. We propose using action reconstruction as a scoring criterion for synthesized reasoning traces, yielding more causally faithful reasoning and improved downstream action prediction across user modeling tasks. Paper and project page in 🧵

RT @melissapan: Excited to share that MAP has been selected for ✨ICML Oral✨ We look forward to sharing the insights in the paper with the community And much much appreciations to everyone who participated in our study ❤️ MAP won’t be possible without your contribution to open science

Excited to share: MAP has been accepted as 🌟 ICML Spotlight 🌟 We hope MAP can provide data-driven insights that help the communities to work on various under-explored research directions around agent systems! Huge thanks & congrats to my amazing co-authors. See you all at Seoul! 🫡

RT @MangQiuyang: Open-ended coding training data may no longer be the bottleneck: AI can scale open-ended tasks—and even outperform human-expert curation. FrontierCS team is releasing FrontierSmith: a system for synthesizing open-ended coding problems at scale. Starting from closed-ended coding tasks, FrontierSmith mutates, filters, and builds runnable optimization environments for long-horizon coding agents. In our experiments, FrontierSmith data trains stronger models than human-curated open-ended data on FrontierCS and ALE-bench. Blog: Paper: Code: Model:

RT @ziming_mao: 🚀 Excited to release mKernel: a set of fast multi-node, multi-GPU fused kernels. 💻 Code: 📝 Blog: mKernel fuses compute + communication into one persistent GPU kernel, covering both intra/inter-node with GPU-initiated communication. Amazing team: @yangzhouy, Chon Lam Lao, Costin Raiciu, Scott Shenker, @istoica05

RT @LakshyAAAgrawal: Learning from rich textual feedback (errors, traces, partial reasoning) beats scalar reward alone for LLM optimization. GEPA demonstrated this for context-space optimization (prompts and agent harnesses), delivering frontier results at a fraction of the cost of RL. But context-only optimization is bounded by the base model's capability ceiling; weight updates can reach further. Very excited about this new line of work on Fast-Slow Training (FST), which interleaves context and model weight optimization! The idea is a clean division of labor between two interleaved loops: 🔹 Fast loop (context): GEPA reads rich rollout feedback updating the context layer. The context becomes a fast-updating scratchpad of what the model needs to know about this task, right now. 🔹 Slow loop (model parameters): RL updates the model's parameters conditioned on the evolving context. Because the prompt already carries task-specific nuances, the model parameters are freed from absorbing them and focus on what actually generalizes across tasks and pushes the frontier. ⦁ 3× more sample-efficient than RL on math, code, and physics reasoning ⦁ ~70% lower KL divergence from base at matched accuracy ⦁ Plasticity preserved: FST checkpoints respond better to additional RL on new tasks than RL-only ones ⦁ Continual learning across changing tasks (HoVer → CodeIO → Physics) where RL stalls the moment the task switches FST is a direction towards: ⦁ Addressing RL's pain points: entropy collapse, sparse rewards, long-horizon exploration ⦁ Providing a clean channel for rich feedback into weight updates ⦁ Demonstrating model-harness co-evolution ⦁ Discovery: Using fast context updates for broad exploration, while leveraging a continually improving model. Check out the full thread below:

Can LLMs adapt continually without losing base skills? Fast-Slow Training (FST) pairs "slow" weights with "fast" context. FST vs. RL: • 3x more sample-efficient • Higher performance ceiling • Less KL drift (better plasticity) • Continual learning: succeeds where RL stalls

RT @NegarEmpr: 1/ Thrilled to introduce T³: a corpus for RAG over reasoning tasks, built from thinking traces. We show that surprisingly RAG can improve reasoning— with the right corpus. Rag with Transformed Thinking Traces T³ gain by up to 43.9% on AIME 2025-2026. 🔗 🧵

RT @pgasawa: Today, we’re releasing Continual Learning Bench 1.0: the first, realistic benchmark for measuring how AI systems can improve in online settings. Benchmarks today assume models are stateless. Each example is independent, and once a system finishes a task, it moves on as if nothing happened. But deployed AI systems should learn from experience. We tested 10+ frontier systems against novel, expert-validated tasks and find there’s still plenty of headroom for learning. (1/n)