NVIDIA 发布机器人自改进框架 ENPIRE；腾讯披露万卡集群软件

Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv, cappuccinos, and feedback from readers. If you’d like to support this, please subscribe. Subscribe now NVIDIA sets up a crude self-improvement loop for real world robotics:…What if you could take the best ideas from AI agents and put them into the real world?…Researchers with NVIDIA have developed ENPIRE, software to get physical robotics to go through the same kind of autonomous experimentation and execution loop that AI agents go through. The research gives us a taste of what it might look like for a superintelligence to attempt to use robots to instantiate itself in the physical world – though as with all things in robotics, the current examples are suggestive at best. What ENPIRE is: The software is “a harness framework for coding agents that instantiates this physical feedback routine with four core modules: an Environment module (EN) for automatic reset and verification, a Policy Improvement module (PI) that launches policy refinement, a Rollout module (R) to evaluate policies with single or multiple physical robots operating in parallel, and an Evolution module (E) in which coding agents analyze logs, consult literature, improve training infrastructure and algorithm code to address failure modes”. ENPIRE works the same way that coding agents work – a scaffold supervises some physical robots which are asked to complete tasks. The robots try to complete the tasks and attempt different strategies for completing stuff, trying and failing and learning. The system both evaluates their success and also resets itself when they fail. “This closed-loop system transforms real-world robot learning into a controllable optimization procedure that agents can manage, thus minimizing human effort while allowing fair ablations across training recipes and agent variants.” Two of the key ingredients for making this work are an automatic evaluation system to help score “the outcome of each trial without human judgement”, as well as an automatic reset system which “returns the scene to a fresh initial state for the next trial”. (Both of these are tasks which have historically required lots of human effort, and it’s likely that more complicated tasks would also require human effort for evaluation and resets, so in some sense the complexity of tasks a system like this can attack is also defined by our ability to automatically evaluate and reset the system). Hardware details: “Each station comprises two YAM (Yet Another Manipulator) arms from I2RT in a fixed bimanual configuration, a set of cameras, and a single workstation that runs the FastAPI server, policy inference, and the station’s agent.” Each workstation is running a NVIDIA RTX 5090. It works well (on some simple tasks): “Frontier coding agents can autonomously develop a policy to achieve a 99% success rate on challenging, dexterous manipulation tasks in the real world, such as PushT, organizing pins into a pin box, and using a cutter to cut a zip tie,” the authors write. An additional task they test out on is seeing how well the robot can insert GPUs into a motherboard. Some AI systems are better than others, but many AI systems are always better than fewer: GPT-5.5 within Codex and Opus 4.7 within Claude Code trade off with one another for best performance, while Kimi-2.6 lags. There are also compelling returns to scale for agents, with larger numbers of agents (e.g., 8) arriving at higher scoring solutions sooner than others – and sometimes multi-agent setups yield a higher absolute score than a single agent setup, likely due to exploring more of the potential solution space. Challenges remain for fleet instrumentation: “Coding agents do not fully utilize robot resources when they are reading logs, writing code, debugging, or waiting for the language-model backbone. As the number of robots scales, MRU decreases while GPU active utilization increases,” they write. In other words, there are some infrastructure challenges with adding multiple robot agents so things don’t naturally parallelize. Read more: ENPIRE: Agentic Robot Policy Self-Improvement in the Real World (NVIDIA research website). Read more: ENPIRE: Agentic Robot Policy Self-Improvement in the Real World (arXiv). *** Humans are really, really, really bad at anticipating how technologies are built and used:… A quick reminder that today’s hot takes about AI are likely to be wrong…Predicting the future of technology is extremely difficult and our track record of doing it effectively is very poor, points out Matthew Tokson, Associate Dean for Research, University of Utah S.J. Quinney College of Law, in a short SSRN paper. “Skeptics have often underestimated the likelihood of novel innovations and their potential ramifications for humanity. Others have been overly optimistic about the social effects of new technologies or the strategic benefits of racing to build dangerous new weapons”. Cautionary examples: Many of the world’s experts (e.g., Albert Einstein, Niels Bohr, Robert Oppenheimer) were skeptical that nuclear fission could be achieved in the years immediately prior to it being achieved. Nobel-Prize-winning economist Paul Krugman once said the impact of the internet would be no greater than that of the fax machine. Technologists thought the internet would ultimately be a technology that promoted democracy rather than strengthened autocracies. And despite mounting decades of evidence, many human scientists either rejected human-caused climate change or significantly underestimated its effects. Why this matters – basic lessons: The main lesson here is that people who are a) skeptical AI could bring great changes to the economy, or b) think the effects of AI are going to be universally good, are likely to be wrong. “History does not support complacency about the future impacts of AI”, he writes. “Throughout history, optimists have often been wrong about the social ramifications of new technologies or the strategic benefits of building new weapons. Skeptics have often underestimated the likelihood of novel innovations and their impacts on humanity.” Read more: Artificial Intelligence and the Lessons of History (SSRN). *** Tencent details the software it uses for 10,000-GPU training runs:…ARGUS is a technosignature of broader sophistication…Tencent has released details on ARGUS, software it uses to generate telemetry and debug errors of large sets of chips. What it is: ARGUS is “a low-overhead, fine-grained, always-on tracing and real-time analysis system for large-scale training workloads”. The software is designed to help Tencent collect data on and debug problems that it encounters while training AI systems. It consists of three layers of software: “The Python layer for scheduling and data preparation, the framework layer for phase orchestration, and the GPU runtime layer for kernel execution,” Tencent writes. What Tencent used it for: “We deploy ARGUS on a production cluster of over 10,000 GPUs for more than six months, and demonstrate its practical effectiveness through five real-world case studies, diagnosing compute stragglers, communication link degradation, pipeline bubble amplification, JIT compilation blocking, and compute stragglers masked by communication symptoms”, the company writes. Some of the training runs Tencent mentions include a 4,096-GPU video language model training job (likely a “HunyuanVideo” model), a 512-GPU audio-model training job, and a 12,960-GPU MoE training job (likely a Hunyuan LLM). Why this matters – technical symptoms of broader sophistication: Things like ARGUS are a signature of complicated, large-scale infrastructures where it makes sense to write your own software. While there’s nothing particularly notable about ARGUS – you’d expect to find similar software at any self-respecting frontier AI developer – it’s more interesting for what it says about the maturity of Tencent’s training environment. “ARGUS has been deployed on a 10,

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