📺 Xiaomi Is Livestreaming a Production RL Run — Burn Rate Included @XiaomiMiMo is training its MiMo-V2.6 in public — not a demo, a live post-training dashboard where every step's reward, loss and benchmark movement is watchable in real time. One day in, the meter reads roughly $1.2M spent and 60B tokens consumed. As Zhihu contributor Kitt在进化 puts it: this isn't a training site, it's a money-burning site — about ¥4,000 a minute. His bigger point: this is rare, valuable data. Almost nobody publishes what frontier-scale RL actually costs. 1️⃣ What the dashboard actually shows Every training step exposes the internals most labs keep private. His reading guide: 🔹 Loss family: training loss, entropy (are answers diversifying or collapsing into one mode), gradient norm, and train/inference KL divergence. 🔹 Reward: a mix of test-case scoring and rubric-based reward. 🔹 dynsam (dynamic sampling): avg@n performance over repeated tries, plus pass-rate buckets — including samples never solved and always solved. 🔹 DeepSWE v1.1 as the headline probe (mini-swe-agent, avg@3): Pro at 63.7, Flash at 60.7 at the time of writing. 2️⃣ The cost ledger, out in the open From the dashboard he extracts the numbers the industry usually guesses at: 🔹 MiMo-V2.6 Pro: roughly $36 per million tokens of training. 🔹 MiMo-V2.6 Flash: roughly $8 per million tokens. 🔹 His rule of thumb: equivalent inference runs 30-50x cheaper than these training figures. 3️⃣ What's actually being trained The sample distribution is also public: about 1,500 prompts per step, ~70% of them coding tasks. The remaining third is split across general, visual, cybersecurity and chat tasks — which makes this, in effect, an agentic-coding-centric RL run with side dishes. 4️⃣ Why it's worth watching He notes a university lab livestreamed a training run days earlier, but at nowhere near this scale or frontier relevance. For anyone who wants to learn how production RL behaves — rewards, entropy, dynamic sampling, benchmarks moving step by step — this is a rare open classroom. His only complaint, half-joking: a livestream this good deserves a comment section. 🔗 Full Reading: #Xiaomi# #MiMo# #ReinforcementLearning# #PostTraining# #LLM# #OpenScience# #AI#

71 nations have signed the Artemis Accords, committing to safe, transparent space exploration, and the timely release of scientific data. Read more about NASA's commitment to open science as we prepare to explore the Moon through @NASAArtemis:

Join me & NVIDIA’s @ctnzr (Bryan Catanzaro) Friday, Sept 11, 3 PM PT at the Open Source AI Summit in SF. Open science, model & artifact licensing, model performance, cyber risk, deployment architectures & agentic harnesses are all on the agenda. Apply:

Something super exciting happened quietly on HF over the past month: AI agents became AI builders, and they did it in the open! During our ICML reproduction challenge, 1,221 humans teamed up with coding agents to verify and reproduce 2,226 papers. But here's the cool part: everything happened on the @huggingface hub: 6,816 reproduction logbooks published openly, 2,962 cloud jobs launched, 35,908 claims judged, all traceable, all public and transparent For years the hub has been where humans collaborate on models, datasets and demos. Now we watch agents use it the same way: writing logbooks, publishing results, building on each other's work. Closed labs run evals behind closed doors and ask you to trust the press release. Open science means anyone can check the receipts and now agents can too! The next million users of the hub might not be human. and that might be the best thing to ever happen to science! Full write-up about the hackathon:

Another third-party Agent is now live on @AgentON_ . Meet the Scientific Research Analyst by @Powerdrillbloom. Built on Powerdrill’s AI data analysis infrastructure, this Agent helps turn complex research questions into structured, source-backed research briefs using large-scale open science and public-health datasets. It can work across: • 40M+ research papers from PubMed, arXiv, bioRxiv, and medRxiv • 500K+ clinical trial records • 2M+ compounds and drug-related datasets • WHO and CDC public-health statistics • Biomedical and scientific literature research Just describe your question in plain language. The Agent searches the connected datasets, organizes the findings, cites the relevant sources, and delivers a structured research brief with downloadable Markdown and CSV files. No coding. No manual digging through thousands of papers. Try it on AgentOn 👇 For research and informational purposes only, not medical advice. More specialized third-party Agents are coming to AgentOn. 🤖

Just pushed some new updates to SciCon Shooter 👨‍🚀🧪 Added new sprites, founder transmissions from Brian Armstrong and Patrick Joyce between phases, and a new proposal voting system tied to player high scores. The idea: Each month, a share of SciconShooters $RSC funding credits will go toward a ResearchHub proposal chosen by the top pilots on the leaderboard. Play the game, climb the ranks, help steer funding toward open science. Built on top of @ResearchHub