S1: A $6 R1 competitor?

What S1 Is and How It Relates to R1

• S1 is a 32B “reasoning-style” model trained cheaply by distilling thought traces from a stronger model (Gemini), fine‑tuned on ~1k high‑quality chain‑of‑thought examples.
• Several commenters stress it is not the same paradigm as DeepSeek R1: S1 is fully supervised distillation from a powerful oracle; R1 is RL with a weaker judge, potentially usable on new tasks without an oracle.
• Some call S1 “just cheap distillation” and even a “marketing” attempt to ride the R1 brand; others find it notable that so little curated data and compute can match o1-preview on some benchmarks.

“Wait” Tokens and Test-Time Compute

• A key focus is the “Wait” trick: intercepting the model’s attempt to end <think> and replacing it with “Wait” to force more internal reasoning steps.
• People note this is effectively a way to trade latency for better answers, analogous to beam search or backtracking in older systems.
• Some see it as eerily human-like (“are you sure?” prompts improving answers); others say it exposes how poorly we understand our own models if we have to discover such hacks empirically.

Chain-of-Thought, Reasoning, and Architectures

• Many discuss CoT as a scratchpad or multi-pass rendering: more tokens = more internal computation.
• Ideas floated: separate “thinking” context with its own network; meta-controllers that decide when to stop thinking; hierarchical or MCTS-style reasoning; models that learn when to restart or second-guess themselves.
• There’s debate over whether this is real reasoning or just refined interpolation; some argue current models already approximate “ship computer” capabilities from sci‑fi, others insist they’re still stochastic parrots.

Distillation, Cost, and Benchmarks

• Commenters emphasize that the headline “$6 training” ignores the huge cost of the original oracles; distillation is cheap because the expensive work has already been done.
• Concern: if new SOTA models can be cheaply distilled by competitors, the economics of billion‑dollar training runs may be unattractive.
• Skepticism about benchmarks: models can overfit to popular benchmarks, making reported “breakthroughs” less general than they appear.

Running S1 and R1 Locally

• Discussion of GGUF conversions and quantization; several users report S1-32B quantized runs fine on consumer GPUs, with mixed quality reports (some see repetitive think/answer loops).
• Others note tiny reasoning models (e.g., ~1–2B distilled R1 variants) now run acceptably on older, GPU‑less hardware, suggesting a rapid drop in the hardware bar.

National Security, Hype, and AGI

• One thread strongly criticizes framing AI as a national security silver bullet, seeing current systems as glorified ML, fragile and unsafe for mission‑critical defense.
• Others respond with concrete military uses already in play or near‑term: autonomous or semi‑autonomous drones, smart munitions, swarm coordination, surveillance, propaganda, intrusion detection.
• Broader argument: even “fake intelligence” is dangerous if it can replace workers or scale surveillance and repression; skeptics counter that true extrapolative creativity and robust deployment are still lacking.

GPU Economics and Possible Bubble

• Several commenters question the “more H100s = more value” narrative, pointing out hardware depreciation and historical bubbles (dotcom, tulips).
• Some argue big players are burning compute inefficiently due to valuation pressure and Goodhart’s law (spend on GPUs becomes the success metric).
• Others note real progress in distillation and efficiency: if 100× cheaper inference arrives, near‑term demand for extreme GPU build‑outs could fall before new use cases catch up.

Intellectual Property, “Distealing,” and Access Control

• Concern that small curated CoT sets (on the order of 1k examples) make “distealing”—unauthorized distillation from closed APIs—nearly impossible to prevent.
• Proposed defenses: heavy rate-limiting, identity verification, per‑account caps and clustering suspicious usage; others doubt this will stop determined actors.
• Some argue distillation from commercial APIs is ethically fine and akin to expert teaching; others point to the irony of companies that scraped the web now objecting to others scraping their models.

Societal Impact and Jobs

• Several long subthreads debate AGI timelines and acceleration: some foresee a near‑term “takeoff” and are openly terrified; others think fears are overblown or poorly specified.
• A recurring worry is AI‑driven mass unemployment and “technofeudalism”: intelligence at scale in the hands of employers and elites, with no clear path to UBI or new mass employment.
• Anecdotes appear of office workers quietly automating 90%+ of their knowledge work with off‑the‑shelf tools, reinforcing the sense that white‑collar work is already under real pressure.