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July Papers: Subliminal Learning, Mixture of Recursions and Dataset Curation

As July brought tennis at Wimbledon, so too did the ML world serve up a volley of research. This month, we took an eagle-eyed approach—or, perhaps, Hawk Eyed approach—to three papers.

In our first paper, Subliminal Learning addresses the question, "Can we control or filter the distillation training data so that a student learns desirable properties but avoids picking up undesirable traits?" The authors conclude that the student learns all the teacher's traits, whether they're desirable or not!

Next, Mixture of Recursions brings a twist to token-level computation: instead of fixed-depth processing, the model learns to recurse adaptively, allocating compute per token dynamically and efficiently—like a rally whose length depends on the importance of the point.

Last up is DataRater, where the problem of dataset quality is addressed. A 'rater' is meta-learned to curate training data without manual filtering—an ace for data-centric AI.

June Papers: Gradient Norms, LLM Reasoning and Video Generation

This June not only brought us very hot and sunny days (at least here in the UK), but also an excellent selection of new and exciting ML research! Out of the many good candidates, this month we selected three papers, covering quite a lot of different ground.

In the first paper, Why Gradients Rapidly Increase Near the End of Training, a researcher from FAIR explores the puzzling phenomenon of increasing gradient magnitudes during training, offering an elegant mathematical explanation and a simple remedy.

Next, in ProRL, NVIDIA researchers dive into the evolving topic of large language model reasoning, showing how prolonged reinforcement learning can indeed introduce novel reasoning abilities.

Finally, we look at AAPT, a fresh approach from the ByteDance Seed team that turns pre-trained offline diffusion models into real-time video generators via adversarial post-training.

Optimal Formats and the Cube Root of the PDF

Your boss emails you a point in 128-billion-dimensional space. "Llama 3.1 8B," the message reads. "A not-so-large language model in bfloat16. But it's too big. Trim the fat (ASAP)." You open up your toolbox: quantisation, sparsity, distillation.

Quantisation comes first, with two problems. First, you must choose a space smaller than a 128-billion-dimensional binary number for the model to sit in. Second, you need to find a good point in that space. In our recent work on optimal formats for weight quantisation, we've had a crack at the first question.

In this post, we'll learn how to construct optimal formats for known scalar distributions via the "cube root rule". We'll start with a recap of an existing format that claims optimality for the normal distribution. Then we'll explore the cube root rule — a non-intuitive result from the 1950s — and use it to build our own quantisation formats for scaled normal, Laplace and Student's t distributions.

May Papers: Parallel scaling, Evolving code, Understanding LLM reasoning

Hurtling past the NeurIPS submission deadline into the summer months, we switch from huddling around server rooms to keep warm to babysitting experiments whilst basking in the sun. We've had a bumper month of papers to sift through and once again we offer summaries of a few of our favourites.

First, Parallel Scaling Laws for Language Models proposes a novel method of scaling compute with language models inspired by classifier-free guidance that finetunes a model to run multiple forward passes with different learned vector prefixes. We also looked into AlphaEvolve, an evolutionary algorithm from Google DeepMind that generates and refine prompts for Gemini that can advance the state-of-the-art in algorithm design.

Since it has been a particularly exciting month for contributions on LLM reasoning, we picked two papers to dive into deeper. In Soft Thinking the authors attempt to improve on prior work sampling continuous token embeddings rather than discrete tokens during reasoning phases of text generation. Finally, in Spurious Rewards they find that even rewarding random answers can improve reasoning ability, potentially forcing us to reconsider how we understand post-training techniques to improve the use of test-time compute.

April Papers: Motion Prompting, Mamba Reasoning and Modeling Rewards

April has been a busy month for the AI research community, with ICLR (the first of the "big three" AI conferences of the year) taking place in Singapore. We're pleased to share summaries of a few of our favourite papers we've seen this month.

First up, Motion Prompting introduces flexible spatio-temporal trajectories, or "motion prompts", as a powerful new way to control nuanced dynamic actions and motion in video generation, overcoming the limitations of text prompts. This is followed by Inference-Time Scaling for Generalist Reward Modeling, which presents Self-Principled Critique Tuning (SPCT), a method that powers DeepSeek-GRM—a generalist reward model capable of generating adaptive, high-quality rewards and achieving strong performance gains through scalable inference-time compute. Finally, M1 looks at using a Mamba-based architecture to tackle reasoning problems, as a more computationally-efficient approach when compared to transformers with chains-of-thought.

March Papers: De-Norming, Skill-Scaling, Over-Training and Drug-Generating

We've enjoyed March, bringing improving weather and many excellent ML papers to keep us busy. As usual, we're here to share summaries of four of our favourites.

First, Meta share their work that successfully removes the need for LayerNorm in transformers, replacing them with a reduction-free \(\tanh\) (de-norming). This is followed by two papers on scaling - studying the different scaling laws for skill-based vs knowledge-based downstream tasks (skill-scaling), and whether pretraining can go on too long, making downstream performance worse (over-training). Finally, EPFL share a flow-matching GNN model for generating small molecules for drug design (drug-generating).

February Papers: Learning to Scale

Welcome to Papers of the Month! This time around, our monthly selection of ML papers revolves around the central theme of scale – and learning how to scale efficiently. Scaling-laws for LLMs, multi-scale quantisation training and scaling test-time compute: it's a rich buffet!

The first paper, Distillation Scaling Laws, presents a thorough study of distillation for Language Models, with the aim of estimating how student performance scales as a function of model size and amount of distillation data used -- offering very useful insights, in an era where distillation pre-training of LLMs is becoming more and more widespread to improve "capability per watt".

The problem of computational efficiency and cost reduction is also at the heart of Matryoshka Quantisation, DeepMind's solution for training a quantised model that can then be easily served at different lower numerical precisions, by leveraging the nested structure of integer data types. And if you are a quantisation geek like we are, make sure to also read our summary of ParetoQ, a new unified framework to investigate the scaling laws that govern the trade-off between quantised model size and accuracy in extremely low-bit regimes.

Finally, we jump from training scaling laws to scaling up test-time compute, with a paper that introduces a recurrent block in LLMs at test-time to allow the model to perform iterative reasoning in latent space, without verbalizing its intermediate thoughts, to improve its performance.

We hope you enjoy these month's papers as much as we did! If you have thoughts or questions, please reach out to us at @GCResearchTeam.

January Papers: More Like "Reas-anuary Papers"

New year, new Papers of the Month! Kicking off 2025, it's apparent that reasoning and test-time compute are the hot topics on the block, with much research investigating how to best use these new methods to improve LLM capabilities.

We start with Titans, which introduces a memory module to architectures that can be updated during inference. This results in a hybrid between attention mechanisms and recurrent models, and unlocks the ability to handle really long sequence lengths.

Evolving Deeper LLM Thinking explores evolutionary search strategies to scale test-time compute, outperforming other inference strategies in natural language planning tasks.

Transformer-Squared is a novel approach that adapts LLMs for new tasks by selectively adjusting the singular components of their weight matrices, helping broaden LLMs' abilities to handle diverse tasks with fewer parameters and greater efficiency.

Finally, we look at two recent models from DeepSeek; DeepSeek-V3 and DeepSeek-R1. Given this double-release is packed with so much information, today we'll only cover the high-level details on the innovations described in the papers and their impact on efficiency and model performance — we will release a new blog post soon with a deep-dive into DeepSeek's recent publications.

We hope you enjoy these month's papers as much as we did! If you have thoughts or questions, please reach out to us at @GCResearchTeam.

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Llama 3.2 Vision — A Deep Dive

Vision-Language Models (VLMs) allow LLMs to "see", but how do they work? In this post, we'll walk through the model changes needed to turn an LLM into a VLM for inference. To understand the LLM starting point, please see A transformer walk-through with Gemma, as we shall assume that content here.

Problem — Text generation, conditioned on an image: take an RGB image (below) and a short string prompt "What colour shirt is the person to the left of the laptop wearing?", then use an already-trained VLM (Llama-3.2-11B-Vision-Instruct by Meta) to generate an answer to the prompt.

Image of four people looking at a laptop

December Papers: Spend Your FLOPs Wisely

Welcome to Papers of the Month — Graphcore Research's effort to bring you our pick of the most interesting ML papers. In December we noted a collection of papers which took innovative approaches to allocating compute (FLOPs) to input data.

We start with the Byte Latent Transformer. This modifies the standard transformer to operate on patches, which comprise a variable number of input bytes, as determined by an entropy metric. The consequence of this is that compute is dynamically allocated towards "harder input data". This has some similarities with the Concept Model architecture, which also uses a flexible intermediate representation. The model performs autoregressive sentence generation in this modality-agnostic space, rather than token space.

The Memory Layers architecture allows extra parameters to be added to a model without increasing FLOPs. Decoupling these resources gives model designers more control (e.g. for co-design, to fit their hardware resources) and potentially facilitates more effective models in general.

Finally, the Phi-4 paper presents a rather different FLOPs angle: spending compute in the data-generation process to create higher quality data, leading to "student" models that (in some domains) out-perform their "teachers".

We hope you enjoy these month's papers as much as we did! If you have thoughts or questions, please reach out to us at @GCResearchTeam.