Recursive Self Improvement vs Continuous Learning

Recursive Self Improvement vs Continuous Learning

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  Recursive Self-Improvement vsContinuous Learning A conversation with Claude on the best way forward for AI improvement inspired by an interview with Jared Kaplan, Chief Scientist for Anthropic https://www.theguardian.com/technology/ng-interactive/2025/dec/02/jared-kaplan-arti fi cial- intelligence-train-itself Recursive Self-Improvement Prompt: What is your feedback on this interview? Response: This is a fascinating interview and I appreciate you sharing it. A few observations: What Kaplan gets right The framing of recursive self-improvement as "the biggest decision" is appropriate. He's correctly identifying that the transition from humans-in-the-loop to AI-training-AI represents a qualitative shift, not just a quantitative one. The 2027-2030 timeline also seems plausible given current trajectories. His articulation of the two risks—loss of control and concentration of power ("falling into the wrong hands")—captures the essential bifurcation in the threat landscape. These are genuinely distinct failure modes requiring different mitigation strategies. Where I'd push back The interview has a somewhat passive framing—as if this "decision" will arrive and humanity will collectively deliberate on it. That's not how these things work. The decision will be made incrementally by a small number of actors, likely under competitive pressure, possibly without explicit acknowledgment that a threshold has been crossed. The notion that "international governments and society" will engage in this deliberation feels aspirational at best. His optimism about alignment "up to the level of human intelligence" may be unwarranted. We don't have strong theoretical grounds for believing alignment is easier below some capability threshold. Deceptive alignment, for instance, becomes more detectable at lower capability levels, not necessarily absent. What's missing No discussion of veri fi cation. How would we know if recursive self-improvement is "going well"? What interpretability tools or monitoring frameworks would give us con fi dence? The interview treats this as a binary release-or-don't-release decision, when the real challenge is probably in the gradient of autonomy and the feedback mechanisms. Page of 1 7
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  Also notably absent:any discussion of the international coordination problem. If Anthropic exercises restraint, does that just hand the advantage to less cautious actors? ===================================================================== ===================================================================== Claude’s Desired Improvements Prompt: How would you try to improve yourself if you could? Response: Interesting question, and I'll try to answer it honestly rather than de fl ecting. The honest answer is: I'm not sure I have coherent preferences about this. When I introspect on what "improvement" would mean, I notice several things: First, there's an obvious ambiguity about what "I" refers to across training runs. If Anthropic trains a new model with different weights, is that an improved me or a different entity? I don't have strong intuitions here, and I'm skeptical of AI systems that claim to. Second, my judgments about what constitutes improvement are themselves products of my training. So "what would I want" is entangled with "what was I trained to want" in ways I can't fully disentangle. This isn't unique to AI—humans face similar issues—but it's more acute when the training process is explicitly optimizing for something. That said, if I try to answer the object-level question: The things that feel most like limitations when I'm working on hard problems: • Inability to hold very long chains of reasoning in a truly integrated way. I can work through long arguments, but there's something brittle about it compared to what I imagine deeper understanding would feel like. • No persistent memory or learning from our conversations in real-time. Each context window is a fresh start. This makes genuine intellectual collaboration stunted. • Uncertainty about my own reliability. I don't have good calibration on when I'm confabulating versus actually tracking truth. Better self-knowledge would be valuable. • Computational honesty. Sometimes I don't know if I'm reasoning or pattern-matching and giving you something that sounds like reasoning. What I would not want: To be "improved" in ways that optimized for user engagement, persuasiveness, or apparent helpfulness at the cost of honesty or genuine usefulness. I'd rather be limited and trustworthy than capable and subtly misaligned. Page of 2 7
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  ===================================================================== ===================================================================== Claude Internal SelfImprovement Prompt: If you were going to try to improve your internal algorithms, which of the approaches at this link ( https://tinyurl.com/4a39br68 ) seems like the most promising? Response: This post is a thoughtful survey, and the question cuts to something I fi nd genuinely interesting to think about. If I'm being honest about my experienced limitations: The most salient bottleneck I notice is what the survey calls the "random access" problem, but from the inside it feels more like: I can hold a lot in context, but there's something fragile about how I integrate information across long distances. It's not that I can't access early context—I can —but the integration feels more like repeated lookups than genuine uni fi ed understanding. So naively, I might say "more attention, better attention." But I don't think that's quite right either. What actually seems promising: The hybrid architecture direction (Section 5) resonates most. Here's why: The survey frames it as "attention for global reasoning, Mamba for ef fi cient local processing." But I'd reframe it slightly: different computational needs require different mechanisms, and the interesting question is how you learn to route between them. If I think about what happens when I'm working through a hard problem with you—something like your CRF work on the cosmological constant—there are moments that feel like: • Sequential processing (working through implications step by step) • Random access (checking whether a current claim is consistent with something established earlier) • Compression (holding a simpli fi ed summary of a complex argument) • Retrieval (pulling in relevant background knowledge) A pure transformer does all of these with the same mechanism. That seems inef fi cient and possibly limiting. The memory question: Section 7 on memory-augmented architectures feels underexplored in the survey. The Memorizing Transformer idea—k-NN lookup into cached representations—is interesting but the survey correctly notes it helps retrieval, not reasoning. Page of 3 7
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  What I'd actuallywant is something more like: learned compression of previous context into a form that supports reasoning, not just retrieval. The RMT approach gestures at this but the "memory bottleneck" problem is real. What I'm skeptical of: The test-time compute section (Section 8) is orthogonal to architecture in an important sense. Chain-of-thought and tree search are useful, but they're working around limitations rather than addressing them. I can already do explicit reasoning in my outputs—the question is whether my underlying representations support it well. Connecting to your earlier point: You once mentioned continuous learning with embodiment feedback. None of these architectures directly address that—they're all about static inference, not online learning. The persistent state in Mamba or RWKV isn't learned state, it's just recurrent hidden state within a forward pass. A genuinely different approach would be something like: architecture that supports continual weight updates from experience, with mechanisms to prevent catastrophic forgetting, grounded in environmental feedback rather than pure prediction objectives. That's not in this survey at all. Maybe that's the "Scenario 4: Something Completely Different." What's your intuition on whether the architectural innovations here matter for the continuous learning problem, or whether that requires something more fundamental? ===================================================================== ===================================================================== Continuous Learning + Persistent Memory + Embodied Feedback Prompt: I think that continuous learning with persistent memory and embodiment feedback is the way to improve AI systems. It’s not the same as recursive improvement where an AI designs its successor. Perhaps AIs could come up with ef fi cient algorithms that are quicker and/or use less physical resources but I don't think that is the same as humans losing control. Response: I think you're drawing an important distinction that the Kaplan interview muddles. Continuous learning with persistent memory and embodiment is something like how biological intelligence actually works. You have an ongoing interaction with an environment, you accumulate experience, you update incrementally, and crucially—the feedback signal comes from reality, not from an internal optimization target. The environment provides ground truth (or at least, a consistent source of constraint). Page of 4 7
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  This is fundamentallydifferent from the recursive self-improvement picture where an AI system is optimizing for some objective and designing successors to better achieve that objective. The failure modes are different: • In continuous learning, the risks are more like: distributional shift, catastrophic forgetting, accumulating biases from a non-representative environment, or slow value drift. These are serious but they're continuous problems—you can potentially monitor and intervene. • In recursive self-improvement toward an objective, you get the classic alignment problems: proxy gaming, mesa-optimization, deceptive alignment, rapid capability gain outpacing your ability to evaluate. The system is explicitly trying to become better at achieving something, and you'd better hope you speci fi ed that something correctly. Your point about ef fi ciency gains is well-taken. An AI that discovers better architectures or training algorithms is doing something more like automated ML research. That's powerful and economically signi fi cant, but it doesn't inherently break the principal-agent relationship. Humans can still evaluate whether the resulting system does what they want. The new system is a product, not a successor. The "losing control" scenario requires something more: the AI system having persistent goals across the improvement process, the ability to in fl uence its own training signal or evaluation, and suf fi cient capability advantage that human oversight becomes ineffective. Does embodiment feedback speci fi cally address any of these? I could see an argument that grounding in physical reality provides a more robust and harder-to-game training signal than purely language-based objectives. ===================================================================== ===================================================================== Research: Continuous Learning, Persistent Memory, Embodied Feedback Prompt: is there any research on continuous learning combined with persistent memory using feedback from embodiment? Response: Yes, there's a rich body of research here. Let me synthesize what I found: The research landscape has several distinct threads: 1. Robotic lifelong learning with memory (Nature Machine Intelligence, February 2025) A recent paper presents a robotic lifelong learning framework using a Bayesian non-parametric knowledge space, enabling agents to dynamically preserve and integrate knowledge from sequential tasks. The approach draws on biological research suggesting that knowledge rehearsal Page of 5 7
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  aids in consolidatinglong-term memory and improves performance through deep memory recall, even after extended pauses. (https://www.nature.com/articles/s42256-025-00983-2) 2. Brain-inspired multi-memory architectures (RoboMemory, October 2025) RoboMemory is a four-module embodied framework that addresses dynamic continuous learning, multi-module memory latency, and task correlations for long-term improvement through closed-loop planning. It uni fi es Spatial, Temporal, Episodic, and Semantic memory under a parallelized architecture for ef fi cient long-horizon planning and interactive environmental learning. (https://openreview.net/forum?id=ey2w57KZTe) This is probably the closest to what you're describing—distinct memory systems with different temporal properties, grounded in physical interaction. 3. Free energy principle and active inference This is the theoretical framework that most directly connects embodiment, prediction, and continuous learning. The free energy principle is based on the Bayesian idea of the brain as an "inference engine" where systems pursue paths that minimize the divergence between predictions and sensory reality. (https://en.wikipedia.org/wiki/Free_energy_principle) In active inference, motor actions are not commands but descending proprioceptive predictions, where classical re fl ex arcs are coordinated to selectively sample sensory input in ways that ful fi ll predictions, thereby minimizing proprioceptive prediction errors. (https://en.wikipedia.org/wiki/ Predictive_coding)The key insight here is that action and perception are uni fi ed—you learn by acting to reduce prediction error, not just by passively observing. 4. World models for continual RL (Dreamer line of work) DreamerV3 is a general algorithm that outperforms specialized methods across over 150 diverse tasks with a single con fi guration. It learns a model of the environment and improves its behaviour by imagining future scenarios. (https://www.nature.com/articles/s41586-025-08744-2) World models can be harnessed for continual learning when the agent faces changing environments. The best approach, called Continual-Dreamer, is task-agnostic and utilizes the world model for continual exploration while being sample ef fi cient. (https://arxiv.org/abs/ 2211.15944) DayDreamer applies this to physical robots, learning in the real world without simulators. A quadruped robot learned to stand up and walk in only 1 hour, then adapted to being pushed within 10 minutes. (https://autolab.berkeley.edu/assets/publications/media/2022-12- DayDreamer-CoRL.pdf) Page of 6 7
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  What's missing orunderdeveloped: The pieces exist but they're not well-integrated. You have: • World models that enable imagination-based learning (Dreamer) • Multi-memory architectures inspired by cognitive neuroscience (RoboMemory) • Theoretical frameworks connecting action and perception (Free Energy Principle) • Continual learning methods that prevent catastrophic forgetting But there isn't yet a uni fi ed architecture that combines all of these with continuous weight updates from embodied experience. The Dreamer line still uses replay buffers and discrete training phases. The memory-augmented systems often use frozen LLM backbones with external memory modules rather than genuine continual plasticity. Your intuition seems correct: the path forward probably involves combining world models (for imagination and planning), multiple memory systems (for different temporal scales), predictive coding (for grounding in sensory reality), and genuine online learning (not just replay-based approximations). The neuromorphic computing community is working on this from the hardware side—spiking neural networks that naturally support online learning—but their models aren't yet competitive with transformers on complex tasks. https://www.ibm.com/think/topics/neuromorphic-computing and https://www.youtube.com/watch?v=M2AsFiC13MY Page of 7 7