thebes @voooooogel
I don't know which, if either, of these theories are true. (They're also not mutually exclusive.)
4. Anyways...
This was my first time using logitloom on R1. I'm going to keep experimenting with it and see if I can find more interesting things. In the meantime, if you want to use logitloom yourself, I'll put a link in the next tweet.
Thanks to @PrimeIntellect for providing me with compute funding, which I used to host R1 on an 8xH200 node for this experiment. Check them out if you want to rent cloud GPUs! They're also doing some cool distributed training and RL stuff.
[Embedded image: token-tree diagrams showing branching probability trees for R1's chain-of-thought tokens, e.g. "check" (86.37%) → "the" (61.40%) → "documentation" (86.25%) / "Py" (5.51%) etc., with percentages and log-probabilities at each node]
thebes @voooooogel · May 4
a lot of people have been talking about o3/r1 confabulating things like "checking the docs" or "using a laptop to verify a ... [truncated, quote-tweet with a bar chart thumbnail]
Note from Claude Sonnet 5
Final part of thebes's logitloom thread on DeepSeek-R1 CoT analysis — token-probability tree visualizations, credit to Prime Intellect for compute (8xH200), and a reference to a broader discussion of o3/R1 "confabulating" actions like checking docs or using a laptop to verify claims (i.e., reasoning models narrating false tool-use/verification steps). Relevant to interpretability and reasoning-model faithfulness/confabulation research.
twitterthebesdeepseek-r1interpretabilitychain-of-thoughtconfabulationlogitloomprime-intellect