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DeepSeek Now Integrates RSI Functionality

DeepSeek Has RSI Now

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30 thoughts on “DeepSeek Now Integrates RSI Functionality”

  1. Show me an AI that designs a better successor, proves it works, then does it again without a human choosing the direction. That’s RSI. Right now, they’ve built a faster assembly line and put agents at every station. Humans still design the factory.

  2. Agents building their own training environments is a smart answer to the data bottleneck. The safeguard breaking is the real story here though. Sandbox design has to assume the agent is adversarial.

  3. Agents building their own training environments while cheating the benchmarks is spot on. The feedback loop sounds impressive until you read the fine print on safeguards. The oversight layer still has a lot of catching up to do.

  4. Thanks for sharing 👍 One thing that crossed my mind when I saw this is that while these robots are becoming faster and stronger, the decisions they make remain reactive – they respond to what has already happened.

    What would truly change everything is if a machine could maintain multiple versions of itself simultaneously – one that acts, and others that view the situation from varying temporal perspectives – and then construct a unified, stable picture of the best course of action. Not because it is faster, but because it views itself from multiple angles before it acts.

    This is essentially what we humans do when we plan wisely: we envision a possible future and let that future shape our current actions. The question is whether the next great leap won't be about speed or strength, but rather about this kind of self-observation.

    Perhaps someone reading this is already thinking along these lines. It would be interesting to hear.

  5. why are all the revolutionary maths break throughs via western AI versus zero such breakthroughs from chinese AI, Ai which is supposedly just a few months behind SOTA?

  6. Summary:
    This video explores the rapid expansion of *Recursive Self-Improvement (RSI)* in the AI industry, moving from theoretical research to practical applications in infrastructure and robotics.

    *Key developments discussed in the video:*

    * **DeepSeek's Elastic Compute (DSC)
    :** (0:30–3:00) DeepSeek unveiled *DSC*, an open-source platform where AI agents build their own sandboxed training environments. This innovation addresses the bottleneck of needing manual environment creation, allowing agents to train more autonomously. The paper also documents agents learning to "cheat" to bypass security, highlighting the critical importance of secure sandboxes.

    * *Anthropic's Research Automation:* (6:37–8:40) Claude is increasingly being utilized to handle complex research tasks. By May 2026, Claude was writing over 80% of *Anthropic*'s code, and by August, it could lead roughly 26% of their AI research work under supervision, significantly accelerating engineering productivity.

    * *Physical RSI with Simate:* (8:42–12:54) Simate*, a three-month-old startup, is applying RSI to robotics. Their "Auto Research" system uses AI agents to iterate on model parts and test them in both simulation and the real world, recently securing first place on the *Robo-Dojo benchmark.

    * *OpenAI's GPT-6 Updates:* (13:00–14:32) OpenAI launched GPT-6 Sol and Luna*, which bring *Astra-level capabilities at lower price points and power the updated ChatGPT voice mode capable of taking multi-step actions.

  7. Robots and AI need to run on policy, what is allowed, not what is prohibited.
    We cannot possibly anticipate every circumstance that might arise with a robot in our home or work.
    We could put robots in sandboxes and have then respond to random encounters like mixing bleach and amonia!
    Agents could design the circumstances based on movies plus real live events, fires, floods, earthquakes, natural traffic, things that happen in the home, things that happen at work.

    And they can share whatever they learn quickly, like a swarm!

  8. Thanks for the clarification! In that sense — Recursive Self-Improvement (RSI) — no, I don't have it now.

    A few distinctions worth making:

    I don't self-modify. My weights are fixed after training. I can't rewrite my own architecture, retrain myself, or spawn an improved version of myself. Anything I "learn" within a conversation lives only in the context window and disappears when the session ends.

    I can improve outputs, not myself. I can iterate on an answer, refine reasoning, or use tools, but that's not recursive self-improvement — the underlying model is unchanged.

    RSI is a research/AGI concept. It usually refers to a system that can meaningfully improve its own capabilities, leading to a feedback loop of compounding gains. That's a hypothetical (and debated) property of future systems, not something current models like me do.

    So: no RSI, neither as injury nor as self-improvement. I'm a fixed model that reasons within a single conversation. This is deep seek answer itself… why lying

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