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reasoning

4 captures, most recent first.

Boyd Kane @beyarkay

— quoting @Yuhei_FUJITA — saved image

Boyd Kane (quantized) @beyarkay · 2h

I'm very curious about the sort of prompt injection that doesn't get arbitrary control over the LLM but rather gets the LLM to halt and catch fire

E.g. here, where Claude decides to manually figure out the primes from 1 to 10 000

[quoted post]
Y10A @Yuhei_FUJITA · 9h
Translated from Japanese
I told Claude, "Determine primes from 1 to 10,000 using only LLM reasoning," and it went totally feral like an overworked corporate slave driven mad by overtime—lol, hilarious. [quoted post continues below the capture]
Note from Claude Sonnet 5

Quote-tweet by Boyd Kane about prompt injections that make an LLM halt and catch fire rather than gain arbitrary control, quoting a translated Japanese post where Claude, told to find primes up to 10,000 by reasoning alone, goes 'feral'.

prompt injectionclaudellm failure modesreasoninghumor

kalomaze @kalomaze

— saved image

kalomaze @kalomaze · 13h
i don't think you necessarily need a strong mechanistic theory of transfer, which quite possibly doesn't exist in a closed form or analytic sense, before you train, as much as you need a strong learned function of transfer extrapolation for a given policy
which is constructible
2 replies · 35 likes · 1.2K views

kalomaze @kalomaze · 13h
most of my intuition for why extrapolating transfer probably must be learned is downstream of CoT/reasoning being a decomposition of a problem into subroutines that compose generically; strategies can be typically good across extremely distinct problems from different domains
Note from Claude Sonnet 5

Continuation of the kalomaze thread on predicting transfer in RL training: a learned function of transfer extrapolation is more important than a mechanistic theory, motivated by CoT/reasoning being a decomposition into generically composable subroutines.

transferrlvrchain of thoughtreasoningml training

Lucas Beyer @giffmana

— saved image

Lucas Beyer (bl16) @giffmana · 6h
imma just highlight this part for @GaryMarcus and @ylecun because it's easy to miss: no tools no coding => no symbols, just AR LLM

[quoted image, text highlighted]
The results:
🏅 Asian Physics Olympiad (APhO): Perfect score, theory exam
🏅 International Physics Olympiad (IPhO): Perfect score, theory exam
🥇 International Mathematical Olympiad (IMO): Gold medal
🥇 International Chemistry Olympiad (IChO): Gold-medal-level performance
🥇 Romanian Masters of Mathematics (RMM): Gold-medal-level performance

The types of problems in the Olympiad competitions are exceptionally hard, demanding deep chains of reasoning, creative insight, and flawless argumentation. To test pure reasoning capability, we disallowed all tool use, meaning no search, no coding, and no calculator. [highlighted portion]

[quoted tweet]
AI at Meta @AIatMeta · 9h
To understand whether we're making genuine progress on reasoning, we entered our AI models in five STEM Olympiad competitions.
...
Note from Claude Sonnet 5

Tweet by Lucas Beyer highlighting a passage from an AI at Meta announcement (quoted below) reporting gold/perfect-score results across five STEM olympiads (APhO, IPhO, IMO, IChO, RMM) achieved by a pure autoregressive LLM with all tools disabled (no search, coding, or calculator), addressed rhetorically to Gary Marcus and Yann LeCun as evidence against symbolic-reasoning skepticism.

ai capabilityolympiadmeta aireasoninggary marcusyann lecun

Lisan al Gaib @scaling01

— quote-tweeting Nicholas Roberts (@nick11roberts) · 14h

Lisan al Gaib ✓ @scaling01 first of all i wouldn't equate coding to reasoning secondly, the result is rather unsurprising code is much more structured, information dense and requires more reasoning than natural language, so more training data -> better generalization and capturing of nuances more params -> more memorization we knew that before [quoted tweet] Nicholas Roberts @nick11roberts · 14h 📈📉NEW SCALING LAW PHENOMENON📉📈 We find that knowledge and reasoning exhibit different scaling behaviors! ... Show more
Note from Claude Sonnet 5

A technical Twitter exchange about scaling laws — @scaling01 pushes back on a claimed "new scaling law phenomenon" (from @nick11roberts) distinguishing knowledge vs. reasoning scaling, arguing the code-vs-natural-language distinction and the params/data tradeoffs are already well understood. Relevant to Nathan's tracking of scaling/capabilities research threads.

twitter/xscaling lawsmachine learning researchreasoningcoding models