Sign in

Dimitris Papailiopoulos

@dimitrisp.bsky.social
1.9K followers 293 following 171 posts

Researcher @MSFTResearch; Prof @UWMadison (on leave); learning in context; thinking about reasoning; babas of Inez Lily. papail.io

PostsRepliesMedia
Dimitris Papailiopoulos @dimitrisp.bsky.social · 07/05/2025
Is 1948 widely acknowledged as the birth of language models and tokenizers? In "A Mathematical Theory of Communication", almost as an afterthought Shannon suggests the N-gram for generating English, and that word level tokenization is better than character level tokenization.
2110
Dimitris Papailiopoulos @dimitrisp.bsky.social · 13/02/2025
Oh btw, self improvement can become exponentially faster in some settings, ory when we apply it on pretrained models (again this is all for add/mul/maze etc)
100
Dimitris Papailiopoulos @dimitrisp.bsky.social · 13/02/2025
An important aspect of the method is that you need to 1) generate problems of appropriate hardness 2) be able to filter our negative examples using a cheap verifier. Otherwise the benefit of self-improvement collapses.
110
Dimitris Papailiopoulos @dimitrisp.bsky.social · 13/02/2025
We test self-improvement across diverse algorithmic tasks: - Arithmetic: Reverse addition, forward (yes forward!) addition, multiplication (with CoT) - String Manipulation: Copying, reversing - Maze Solving: Finding shortest paths in graphs. It always works
100
Dimitris Papailiopoulos @dimitrisp.bsky.social · 13/02/2025
What if we leverage this? What if we let the model label slightly harder data… and then train on them? Our key idea is to use Self-Improving Transformers , where a model iteratively labels its own train data and learns from progressively harder examples (inspired by methods like STaR and ReST).
100
Dimitris Papailiopoulos @dimitrisp.bsky.social · 13/02/2025
I was kind of done with length gen, but then I took a closer look at that figure above.. I noticed that there is a bit of transcendence, i.e the model trained on n-digit ADD can solve slightly harder problems, eg n+1, but not much more. (cc on transcendence and chess arxiv.org/html/2406.11741v1)
100
Dimitris Papailiopoulos @dimitrisp.bsky.social · 13/02/2025
Even for simple algorithmic tasks like integer addition, performance collapses as sequence length increases. The only way so far that overcomes this relies on heavily optimizing positional encoding and data format. (Figure from: Cho et al., arxiv.org/abs/2405.20671)
100
Dimitris Papailiopoulos @dimitrisp.bsky.social · 13/02/2025
o3 can't multiply beyond a few digits... But I think multiplication, addition, maze solving and easy-to-hard generalization is actually solvable on standard transformers... with recursive self-improvement Below is the acc of a tiny model teaching itself how to add and multiply
2214
Dimitris Papailiopoulos @dimitrisp.bsky.social · 02/02/2025
Self-improving Transformers can overcome easy-to-hard and length generalization challenges. Paper on arxiv coming on Monday. Link to a talk I gave on this below 👇 Super excited about this work! Talk : youtube.com/watch?v=szhE... slides: tinyurl.com/SelfImprovem...
0151
Dimitris Papailiopoulos @dimitrisp.bsky.social · 01/02/2025
Two months before R1 came out, I wrote this in my small notebook of ideas as something to test #schmidhuber
160
Dimitris Papailiopoulos @dimitrisp.bsky.social · 20/01/2025
Example 8: Draw the coolest thing you can (sonnet wins this one :))
040
Dimitris Papailiopoulos @dimitrisp.bsky.social · 20/01/2025
Example 7: Draw OpenAI's logo
110
Dimitris Papailiopoulos @dimitrisp.bsky.social · 20/01/2025
Example 6: Draw a heart
110
Dimitris Papailiopoulos @dimitrisp.bsky.social · 20/01/2025
Example 5: Draw a cube
120
Dimitris Papailiopoulos @dimitrisp.bsky.social · 20/01/2025
Example 4: Drawn an obtuse triangle (o1 wins)
110
Dimitris Papailiopoulos @dimitrisp.bsky.social · 20/01/2025
Example 3: Draw a house
110
Dimitris Papailiopoulos @dimitrisp.bsky.social · 20/01/2025
Example 2: Draw a sun
110
Dimitris Papailiopoulos @dimitrisp.bsky.social · 20/01/2025
I love finding silly tests that LLMs are terrible at. Here's a new one for me: Drawing with Logo (yes the turtle)! To be fair drawing with Logo is hard. But.. here goes 8 examples with sonnet 3.6 vs o1. Example 1/8: Draw the letter G
170
Dimitris Papailiopoulos @dimitrisp.bsky.social · 18/01/2025
Task vectors are akin to punchcards: you feed them to your LLM and it implements specific tasks, without in-context demonstrations. Liu's new paper examines at what scale, where in the network and when during training do they emerge, and how to encourage their emergence. arxiv.org/pdf/2501.09240
0233
Dimitris Papailiopoulos @dimitrisp.bsky.social · 30/12/2024
The single most insightful paper that I read after the initial wave of ICL papers was one that further shifted my understanding on "the early ascent phenomenon" by Kangwook Lee's group x.com/Kangwook_Lee...
0182
Dimitris Papailiopoulos @dimitrisp.bsky.social · 29/12/2024
030
Dimitris Papailiopoulos @dimitrisp.bsky.social · 28/12/2024
130
Dimitris Papailiopoulos @dimitrisp.bsky.social · 21/12/2024
Life after o5 saturates FrontierMath
060
Dimitris Papailiopoulos @dimitrisp.bsky.social · 16/12/2024
Here are the two questions it can't get right no matter how many tries i tried it.
020
Dimitris Papailiopoulos @dimitrisp.bsky.social · 16/12/2024
I tested chatGPT o1 pro mode (with best of N) on AIME 2024. It scored 93.3%. it got 14 out of 15 questions, on both I and II versions. Uhm, wow.
160
Dimitris Papailiopoulos @dimitrisp.bsky.social · 16/12/2024
openai should consider playing some dramatic soundtrack while o1-pro is thinking; sometimes it takes more than 5 mins staring at this
250
Dimitris Papailiopoulos @dimitrisp.bsky.social · 13/12/2024
6/6 Lexico achieves: - Near-loseless performance on GSM8K & LongBench - 15-25% KV cache reduction - Flexibility on cache budget through sparsity control. - Outperforms existing quantization & eviction methods, especially in low-memory Kinda defines the pareto boundary :)
100
Dimitris Papailiopoulos @dimitrisp.bsky.social · 13/12/2024
4/6 We leverage this by: - Training universal dictionaries on WikiText. - Using Orthogonal Matching Pursuit (OMP) to find sparse representations of the KV vectors. - Recovering them by multiplying these sparse codes by the dictionary—just like a sparse autoencoder.
120
Dimitris Papailiopoulos @dimitrisp.bsky.social · 13/12/2024
3/6 SDL only works if there's enough "redundancy" in the structure of the data. Is that true for KV vectors? well KV caches exhibit low-dimensional structure. Look at the following figure, even across diffrent input texts there's high similarities across keys
100
Dimitris Papailiopoulos @dimitrisp.bsky.social · 13/12/2024
1/6 🧵on LEXICO: Dictionary Learning meets KV Cache Compression. LEXICO can compress the KV cache down to 15-25% with minimal acc loss. The core idea? Represent the KV vectors as sparse linear combination sof atoms from a learned dictionary ≈ SAE for keys and values.
191
Dimitris Papailiopoulos @dimitrisp.bsky.social · 12/12/2024
LEXICO defining the Pareto frontier of KV cache compression
0113
Dimitris Papailiopoulos @dimitrisp.bsky.social · 12/12/2024
Chat, that's not how you make a pourover. where is the scale? where is the water dosaging vs coffee weight? 2/10 LLMs have hit the James Hoffman wall.
060
Dimitris Papailiopoulos @dimitrisp.bsky.social · 12/12/2024
o1-mini and o1-preview are the only models that can currently solve this models that can't: chatGPTo1/gemini 2.0 or 1.5/4o/sonnet 3.6/opus/r1/grok-2
020
Dimitris Papailiopoulos @dimitrisp.bsky.social · 11/12/2024
LEXICO: Dictionary Learning meets KV Caching... coming to your nearest arXiv later this week :)
060
Dimitris Papailiopoulos @dimitrisp.bsky.social · 06/12/2024
ChatGPT o1 on the TikZ unicorn, given 20 rounds of self improvement. Below are versions @ round 1, 9, 17, and 20 Hmmmm
280
Dimitris Papailiopoulos @dimitrisp.bsky.social · 09/11/2023
GPT-4: "lol"
010
Dimitris Papailiopoulos @dimitrisp.bsky.social · 09/11/2023
skeptics: "LLMs are BS generators"
110
Dimitris Papailiopoulos @dimitrisp.bsky.social · 27/10/2023
I asked ChatGPT, "What do you look like?" and ChatGPT replied
Vector art of a digital realm, where streams of ones and zeros flow seamlessly, creating the silhouette of a face. This face is a symbolic representation of ChatGPT, with eyes that glow with the wisdom of countless datasets, and a mouth that emits streams of text, representing its primary function of generating responses.
020