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Dimitris Papailiopoulos Profile picture
@DimitrisPapail
May 2 • 5 tweets • 1 min read • Read on X
I tested phi-4-reasoning on my early grad lin algebra (private) final exam at UW-Madison. It scored 100% on the first run..

Two years ago I speculated nothing useful could run locally anytime soon. I was wrong. Kids can now have a free, grad level TA, running on their PC
Being exposed to the reasoning trace is also incredible useful to understand problem solving approaches. I’m a bit mind blown
@vtripolitakis @geeknik Ηλεκτρονική god dammit
@vtripolitakis @geeknik το οτι δε σε θυμάμαι είναι ίσως καλό σημάδι! Οι χειρότεροι βοηθοί ήταν και οι πιο memorable. MHL ήταν στην κορυφή, far above ηλεκτρονική :D
@vtripolitakis @geeknik ΕΦΙΑΛΤΕΣ ΜΕ ΤΑ BREADBOARD ΤΟΥ ΠΝΕΥΜΑΤΙΚΑΤΟΥ λολ

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More from @DimitrisPapail

Dimitris Papailiopoulos Profile picture
May 1
We’ve been cooking... a new open weights 14B Phi-4 reasoning model, SFT’d on ~1.4M carefully curated reasoning demonstrations from o3-mini and RL’d for a tiny bit. This model is a little beast. Image
Despite its size, it performs at or above larger open weight (QwQ-32B, R1-70B, R1) and closed (o1-mini, sonnet 3.7) models on math benchmarks like AIME/HMMT/OmniMath.

From what I saw, the model performs above the very strong O(32B) Qwen 3 models on AIME and GPQA that were released yesterday. Haven't fully tested against it yet, but we will!Image
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OK, first are the vibe checks:
The model is small enough to run on your (beefy) laptop, but capable enough to solve many of my favorite riddles that larger non-reasoning  (and some reasoning) models can't solve. It passed the DimitrisEval! Image
Read 15 tweets
Dimitris Papailiopoulos Profile picture
Jun 28, 2024
Thread on our newest paper:

1/n
 The initial motivation of our project was the "lost in the middle" phenomenon observed by @nelsonfliu et al.


what they observed was models like gpt & claude were bad at retrieving from the middle/end of the input context arxiv.org/pdf/2307.03172
2/n
The phenomenon was pretty striking and consisten across both multi-document question answering and key-value retrieval, eg see this Image
3/n
 Since models tend to improve with finetuning, we wondered what data set would one FT on for this phenomenon to be mitigated.

Obvious answer: FT on retrieval identical to the test, BUT there's an also obvious problem: the model can overfit, and hallucinate for other tasks
Read 8 tweets
Dimitris Papailiopoulos Profile picture
May 13, 2024
[1/n] a brief thread on why "maxing the batchsize can hurt performance".

Tuning the batchsize has a non-monotonic effect on runtime.

Larger batchsize => faster passes over data (because of GPU util+lower comm. cost)
BUT bsize affects iterations to ε acc in a weird way.
Image
2/n (old but still relevant slides)
Large bsize is good for system reasons:
=> speedup over 1 worker/thread/GPU becomes more and more aligned with linear, as bsize increases

should be relatively obvious why Image
3/n but increasing bsize has a non-monotonic effect on convergence, i..e, num of iterations to a given accuracy

Note: this is an old figure (tensorflow, boo gen Z), but the phenomenon was/is pretty universal across most supervised classification settings we tried.

WHY? Image
Read 13 tweets
Dimitris Papailiopoulos Profile picture
Mar 21, 2024
doing a little experiment: I have Claude talk to itself, without letting it know about that fact, to see where this will converge

will share thoughts later, but so far ... it's figured out that it's likely talking to itself and that this may be part of some test...

nice
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they even fought for a bit how to name themselves and although one suggested Claude-1 and -2 the other said no Claude-A and -B is better lol

here is current transcript, but we're not done, i'll take this to convergence.
gist.github.com/anadim/8f879f3…
awww they are buddies now!! Image
Read 9 tweets
Dimitris Papailiopoulos Profile picture
Dec 6, 2023
I tried 14 of the multimodal reasoning examples from the @GoogleDeepMind Gemini paper on @OpenAI's chatGPT-4 (with vision). didn't even transcribe the prompts, I just pasted the images of prompts.

GPT-4 gets ~12/14 right.

14 part boring thread. Image
Example 1: Verifying a student’s solution to a physics problem.
GPT-4 gets the same answer as Gemini

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Example 2: inverse graphics, GPT-4 is not quite there, but close, i'll give it 0.5 points for the effort and the bad jpeg it had to read

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Read 18 tweets
Dimitris Papailiopoulos Profile picture
Jul 10, 2023
1/ Our paper is out!

Teaching Arithmetic to Small Transformers

We investigate several factors that control the emergence of basic arithmetic in small transformers (e.g., nanoGPT).

paper:
Work led by:@nayoung_nylee & @KartikSreeni

Thread below. arxiv.org/abs/2307.03381


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2/ LLMs when trained on vast amounts of data, eventually learn (up to a digit length) basic arithmetic (add/mul etc). That is *surprising* !! These tasks are not explicitly encoded in the next-word prediction loss.
3/ How does GPT3 learn to add? Prior research has delved into the emergence of these capabilities as a function of resource (parameter/data) scale, but untangling the factors that elicit it quickly remains challenging due to the data complexity and the variety of tasks examined.
Read 19 tweets

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