AI Papers of the Week
Every paper worth reading in AI, hand-picked one week at a time.
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Gemma 3
Gemma 3 is a lightweight open model family (1B–27B parameters) that integrates vision understanding, multilingual coverage, and extended context windows (up to 128K tokens). Here is everything you need to know:
Post Training of LLMs
PoLMs like OpenAI-o1/o3 and DeepSeek-R1 tackle LLM shortcomings in reasoning, ethics, and specialized tasks. This survey tracks their evolution and provides a taxonomy of techniques across fine-tuning, alignment, reasoning, efficiency, and integration, guiding progress toward more robust, versatile AI.
A Few Tokens Are All You Need
Researchers from Tencent AI Lab and The Chinese University of Hong Kong, Shenzhen propose a new approach to boost reasoning in LLMs by only fine-tuning on the first few tokens of generated solutions. Key ideas include:
A Deep Dive into Reasoning LLMs
This survey explores how LLMs can be enhanced after pretraining through fine-tuning, reinforcement learning, and efficient inference strategies. It also highlights challenges like catastrophic forgetting, reward hacking, and ethical considerations, offering a roadmap for more capable and trustworthy AI systems.
Fractal Generative Models
Researchers from MIT CSAIL & Google DeepMind introduce a novel fractal-based framework for generative modeling, where entire generative modules are treated as atomic "building blocks" and invoked recursively-resulting in self-similar fractal architectures:
Emergent Misalignment
New research investigates an unexpected phenomenon: finetuning an LLM on a narrow task can cause it to become broadly misaligned across unrelated domains. By training large models to produce “insecure code,” the authors discovered that these fine-tuned models also offer malicious advice, endorse harming humans, and engage in deceptive behaviors—even when prompted with non-coding questions.
A Systematic Survey of Prompt Optimization
This paper offers a comprehensive survey of Automatic Prompt Optimization (APO)—defining its scope, presenting a unifying 5-part framework, categorizing existing methods, and highlighting key progress and challenges in automating prompt engineering for LLMs.
Optimizing Model Selection for Compound AI
Researchers from Microsoft Research and collaborators introduce LLMSelector, a framework to improve multi-call LLM pipelines by selecting the best model per module instead of using one LLM everywhere. Key insights include:
Brain-to-Text Decoding: A Non-Invasive Approach via Typing
Meta AI’s Brain2Qwerty model translates brain activity into text by decoding signals from non-invasive recordings (EEG/MEG) while users type. Key results include:
ReasonFlux
The ReasonFlux framework is introduced as an efficient way to fine-tune LLMs for complex reasoning, using hierarchical thought processes. Highlights include:
LIMO: Less Is More for Reasoning
Can a handful of examples teach complex math reasoning to LLMs? This new LIMO paper challenges the notion that we need huge fine-tuning datasets for tough reasoning tasks. Key findings:
Syntriever: Training Retrievers with LLM-Generated Data
How can we build a high-quality text retriever without large labeled datasets or access to an LLM’s internals? Syntriever presents a two-stage framework to distill knowledge from a black-box LLM into a retrieval model using synthetic data. Steps:
Docling
[Docling](https://arxiv.org/abs/2501.17887) is an open-source toolkit that can parse several types of popular document formats into a unified, richly structured representation.
DeepSeek-R1
DeepSeek introduces DeepSeek-R1, an advancement in reasoning capabilities achieved through reinforcement learning (RL). It involves two key models: DeepSeek-R1-Zero, which uses pure RL without supervised fine-tuning, and DeepSeek-R1, which combines RL with cold-start data. DeepSeek-R1-Zero demonstrates that models can develop sophisticated reasoning abilities through RL alone, achieving a 71.0% pass rate on AIME 2024 and matching OpenAI-o1-0912's performance. During training, it naturally evolved complex behaviors like self-verification and reflection. However, it faced challenges with readability and language mixing. To address these limitations, DeepSeek-R1 uses a multi-stage approach: initial fine-tuning with high-quality chain-of-thought examples, reasoning-focused RL training, collecting new training data through rejection sampling, and final RL optimization across all scenarios. This resulted in performance comparable to OpenAI-o1-1217, with 79.8% accuracy on AIME 2024 and 97.3% on MATH-500, while maintaining output readability and consistency. DeepSeek also successfully distilled DeepSeek-R1's capabilities into smaller models, with their 7B model outperforming larger competitors and their 32B model achieving results close to OpenAI-o1-mini. This demonstrates the effectiveness of distilling reasoning patterns from larger models rather than training smaller models directly through RL.
Foundations of LLMs
new survey on the foundations of LLMs covering areas such as pre-training, prompting, and alignment methods.
Cosmos World Foundation Model
a framework for training Physical AI systems in digital environments before real-world deployment; the platform includes pre-trained world foundation models that act as digital twins of the physical world, allowing AI systems to safely learn and interact without risking damage to physical hardware; these models can be fine-tuned for specific applications like camera control, robotic manipulation, and autonomous driving.
A Survey on LLMs
a new survey on LLMs including some insights on capabilities and limitations.
On the Overthinking of LLMs
proposes a self-training strategy to mitigate overthinking in o1-like LLMs; it can reduce token output by 48.6% while maintaining accuracy on the widely-used MATH500 test set as applied to QwQ-32B-Preview.
LearnLM
a new LearnLM model that can follow pedagogical instructions, allowing it to adapt its teaching approach based on specified educational needs rather than defaulting to simply presenting information; experimental results show that LearnLM is preferred over other leading models, outperforming GPT-4 by 31%, Claude 3.5 by 11%, and Gemini 1.5 Pro by 13%; this instruction-following approach avoids committing to a single pedagogical framework, instead enabling teachers and developers to specify their desired teaching behaviors while allowing for continuous improvement alongside other capabilities.
Asynchronous Function Calling
proposes AsyncLM, a system for asynchronous LLM function calling; they design an in-context protocol for function calls and interrupts, provide fine-tuning strategy to adapt LLMs to the interrupt semantics, and implement these mechanisms efficiently on LLM inference process; AsyncLM can reduce task completion latency from 1.6x-5.4x compared to synchronous function calling; it enables LLMs to generate and execute function calls concurrently.
Procedural Knowledge in Pretraining Drives Reasoning in LLMs
studies what documents in the pertaining influence model outputs; by looking at the pertaining data, it tries to understand better what kind of generalization strategies LLMs use to perform reasoning tasks; when performing reasoning tasks, it finds that influential documents contain procedural knowledge (e.g., demonstrating how to obtain a solution using formulae or code).
o1 Replication Journey - Part 2
shows that combining simple distillation from o1's API with supervised fine-tuning significantly boosts performance on complex math reasoning tasks; a base model fine-tuned on simply tens of thousands of samples o1-distilled long-thought chains outperform o1-preview on the American Invitational Mathematics Examination (AIME).
TÜLU 3
releases a family of fully-open state-of-the-art post-trained models, alongside its data, code, and training recipes, serving as a comprehensive guide for modern post-training techniques.
Measuring Bullshit in Language Games Played by ChatGPT
proposes that LLM-based chatbots play the ‘language game of bullshit’; by asking ChatGPT to generate scientific articles on topics where it has no knowledge or competence, the authors were able to provide a reference set of how this “bullshit” is manifested.