Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking

Relation to Human Cognition

• Many commenters map chain-of-thought prompting and Quiet-STaR-style “thinking before speaking” to dual‑system theories of mind (fast/automatic vs slow/deliberative).
• Deliberation is likened to creating an internal “workspace” or simulation (mental diorama, OODA loop, agents’ “thought loops”).
• Some note similar ideas in other cognitive frameworks (default mode network, “A brain/B brain”), and suggest brain‑inspired metacognitive agents as a natural next step.

What Is Intelligence? Prediction vs More

• One recurring claim: intelligence is fundamentally the ability to predict future outcomes from past experience; human cortex and LLMs are seen as prediction machines.
• Others push back that this concept omits creativity, abstraction, and symbolic reasoning; prediction is seen as a subset of intelligence, not its definition.
• Extended debate explores free will, randomness, evolutionary context, and whether human minds are “just” material predictors or handle truly immaterial concepts.

Reasoning, CoT, and Quiet-STaR

• Discussion highlights that plain LLMs act like “System 1” (reflexive token emitters); explicit step‑by‑step reasoning is seen as a crude “System 2.”
• Chain-of-thought is framed as planning / scratchpad, helping avoid “talking into a corner” and some hallucinations.
• Concerns: reasoning chains compound per‑step error; symbolic/deterministic components may still be required for reliable long‑step reasoning.
• Some see Quiet-STaR‑like self‑generated thoughts as a possible AGI ingredient; others argue there is no single “missing piece.”

Learning, Memory, and Online Adaptation

• Strong emphasis that human‑like competence likely needs continual, on‑the‑job learning from one’s own actions, not just batch pretraining or occasional fine‑tuning.
• Counterpoint: context, attention, and offline RL/Finetuning might substitute for true online weight updates, though many note current methods are brittle and inefficient.

Consciousness and Inner Speech

• Long subthread compares experiences of inner monologue vs non‑verbal/visual thinking, parallel “streams” of thought, and near‑sleep or meditative states revealing fast/slow processes.
• Some speculate consciousness may be “curated output” of deeper, faster, mostly unconscious processing.

Practical Use and Skepticism

• Mixed views on LLM usefulness: some find them transformative for boilerplate code, SQL, regex, devops scripts, writing, and summarization.
• Others report frequent confident errors on non‑trivial tasks and see a hype bubble; usefulness is often limited to trivial or easily verifiable tasks.

Safety and Methodology

• Dataset poisoning is acknowledged as a real but underappreciated risk.
• Some criticize the paper’s evaluation choices (weak baselines, format issues on GSM8K) and note prior related work on variable computation in neural nets.