TLDR: Initial RL training runs (like those contributing to o3's capabilities) give rise to basic reasoning heuristics (perhaps forming nascent reasoning circuits) that intelligently mimic patterns in the training data. Massively scaling this RL on larger base models presents a potential pathway toward emergent meta-reasoning behaviors, enabling AI to evaluate its own internal states related to reasoning quality. Such meta-reasoning functionally resembles the simulation of consciousness. As Joscha Bach posits, simulating consciousness is key to creating it. Perceiving internal deviations could drive agentic behavior to course correct and minimize surprise. This self-perception/course-correction loop mimics conscious behavior and might unlock true long-horizon agency. However, engineering functional consciousness risks creating beings capable of suffering, alongside a powerful profit motive incentivizing their exploitation.

A Note on Speculation: The core ideas explored here—particularly the specific mechanisms of emergent meta-awareness through scaled RL and its connection to consciousness—represent a speculative hypothesis. It describes one potential trajectory among many possibilities. The actual path of AI development may differ significantly, involve unforeseen breakthroughs, or hit insurmountable roadblocks. This post explores the implications if such a path were to materialize, urging consideration of the profound ethical questions it raises, regardless of the precise technical route.

Current AI Reasoning: Sparks of Generalization, Architectural Ceilings

Modern LLMs learn by encoding statistical relationships between concepts. Reasoning techniques enable them to deploy these statistics in structured ways, simulating logic or creative problem-solving.

We've observed promising sparks of generalization. Models like OpenAI's o3, developed using methods including pure Reinforcement Learning (RL)^[1], demonstrate an ability to recombine learned reasoning patterns in novel ways at test time, as evidenced by its performance on ARC-AGI. This suggests more than mere regurgitation; it points towards a nascent capacity for adaptation, likely underpinned by the formation of rudimentary reasoning circuits.

Yet, this capability has clear boundaries. Performance often plateaus on complex benchmarks demanding fluid intelligence (like the jump from ARC-AGI to ARC-AGI-2). Mor...

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