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).
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.
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.
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.
This month we've got an all-LLM menu of papers for you, with summaries of four great works exploring many different aspects of crafting systems for LLM training and inference.
We start with the surprising result that removing a single weight out of billions can completely ruin a model's ability to generate coherent text. Dubbed "super weights", preserving these weights is essential when quantising models to lower precision.
Also, we discuss how researchers at Meta explored using context parallelism, where the hidden states of the tokens are split across multiple processors and attention is computed using collective operations. They experiment with multiple strategies and find that different strategies should be used during different phases of inference.
Next, we cover an extension of scaling laws to account for numerical precision. The authors find, among other things, that neither 16-bit precision (as in current practice) nor very narrow bit widths (e.g. 4-bit precision) seem to be optimal.
Finally, we have a paper about the critical batch size in LLM training, the point at which increasing the global batch size is no longer helpful. The authors investigate how this value scales with the size of the model and the amount of training data, finding that the amount of training data has a much bigger effect.
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.
This month brought us some exciting developments in improving image-generating models, as well as some interesting insights into how to make large language models think!
We start with promising results from OpenAI on using consistency models for image generation, challenging the well-established denoising diffusion paradigm. While not quite reaching the same performance, these models require orders of magnitude less compute to generate an image, and may provide a very promising future direction.
At the same time, researchers from Google DeepMind were able to achieve state-of-the-art performance in text-to-image generation, by scaling an autoregressive-type transformer to 10.5 billion parameters, stressing the importance of continuous token representations for images.
Finally, since the introduction of OpenAI's o1 model, there has been a growing interest within the research community in understanding how to make large language models reason. In Thinking LLMs, the authors propose a training method to improve the responses from LLMs by eliciting a thought process before generating the answer.
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.
We're pleased to share four papers from different domains: LLM self-correction, FP8 training, generative crystals and optimisation. They are united, somewhat tenuously, by the importance of proper conditioning:
DeepMind researchers explain how conditioning on the wrong distribution during supervised fine-tuning for self-correction is harmful but can be overcome using RL.
A novel Smooth-SwiGLU activation "conditions" the numerics by inserting a scaling factor in just the right place, preventing late-training instability in FP8.
The GenMS architecture that generates crystal structures for materials conditions on high-level textual and low-level structural information for high-quality generation.
SOAP is an evolution of Shampoo, with conditioners in the name and preconditioners forming the eigenbasis for optimisation.
You can be the judge of how tenuous the connection is, but we'd encourage you to check out the summaries first or despite this.
I hope you enjoy these as much as we did. Tell us we're wrong; tell us we're right @GCResearchTeam.
If there's one thing you can count on from Graphcore Research, it's tireless enthusiasm for effective compute utilsation! Our favourite papers from August include:
Spectra, an open suite of 54 LLMs and 500+ intermediate checkpoints from 0.1B to 3.9B, spanning FP16 training, ternary training, and post-training quantisation to 3, 4, 6, and 8 bits. The proposed ternary architecture - TriLM - outperforms BitNet b1.58 models of similar size.
An investigation into two methods for allowing LLMs to improve task performance on challenging prompts by expending more test-time compute. As a result, the authors demonstrate compute-optimal scaling strategies to allocate compute on a per-prompt basis, and show that thoughtful increases in the test-time compute budget for a small model can be more effective than training larger models.
A training dataset derived from a Knowledge Graph where correct answers can always be known, enabling accurate measurement of hallucinations in LLMs. This facilitates an analysis of hallucincation rates and hallucaination detectability as training compute is scaled. So you see, we don't only think about compute!
I hope you enjoy these as much as we did. If you have thoughts or questions, keep the conversation going @GCResearchTeam.
Scaling continues to be a super hot topic of research and our selection of papers for this month all tackle different angles of how to scale models efficiently.
The first paper we cover builds upon the work of muP to give a guide of how we can transfer hyperparameters optimised on small models to the large models we care about, especially as transformer width increases.
Our second chosen paper looks at scaling mixture of expert transformers along the expert dimension. They design an efficient routing strategy that allows them to push the expert number to the extreme for a more compute optimal configuration.
The third paper we discuss addresses the lack of scaling laws for vocabulary parameters in LLMs. They first validate that there exists an optimal vocab size for a given compute budget and then empirically fit power laws to show that vocab parameters should be scaled differently to the other parameters of the model.
Finally, our fourth paper answers the question of whether using long context lengths or retrieval augmented generation is better for scaling in-context learning and if a combination of the two could lead to more efficient inference.
I hope you enjoy these as much as we did. If you have thoughts or questions, keep the conversation going @GCResearchTeam.
Improving transformers is now not "just one area" of machine learning research. This is illustrated by the breadth of papers we got excited about this month, all of which claim to improve upon some aspect of the transformer, but in very different ways.
First, Mamba-2 explores the connection between structured state space models and attention, resulting in a new architecture, Mamba-2. (The paper isn't short, so you get value-for-money with this summary!)
SµPar builds upon the maximal update parameterisation to transfer hyperparameters across different sparsity levels, promising predictable training of sparse models.
CoPE identifies deficiencies in current relative positional encodings, which are critical for turning transformers from set models into sequence models, and introduces a new & richer form of encoding.
Finally, "matmul-free LMs" follow the trajectory of BitNet and BitNet b1.58, removing all matrix multiplies from a transformer LM forward pass (in doing so, they make it an RNN), promising compression & compute efficiency.
I hope you enjoy these as much as we did. If you have thoughts or questions, keep the conversation going @GCResearchTeam.