AI Papers of the Week
Every paper worth reading in AI, hand-picked one week at a time.
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.
HuatuoGPT-o1
presents a novel approach to improving medical reasoning in language models by using a medical verifier to validate model outputs and guide the development of complex reasoning abilities; the system employs a two-stage approach combining fine-tuning and reinforcement learning with verifier-based rewards, achieving superior performance over existing models while using only 40,000 verifiable medical problems.
DeepSeek-V3
a 671B-parameter MoE language model that activates 37B parameters per token, utilizing MLA and DeepSeekMoE architectures for efficient operation; it introduces an auxiliary-loss-free load balancing approach and employs multi-token prediction during training to enhance performance; following pre-training on 14.8 trillion tokens, the model underwent SFT and RL stages, achieving performance comparable to leading closed-source models while surpassing other open-source alternatives; the model requires only 2.788M H800 GPU hours for training, with stable training that avoids any irrecoverable loss spikes.
**Large Concept Models
** - presents an approach that operates on sentence-level semantic representations called concepts, moving beyond token-level processing typical in current LLMs; the model leverages SONAR sentence embeddings to support 200 languages across text and speech modalities, training on autoregressive sentence prediction using various approaches from MSE regression to diffusion-based generation; experiments with both 1.6B and 7B parameter variants trained on 1.3T and 7.7T tokens respectively demonstrate strong performance on generative tasks like summarization and summary expansion.
A Survey on LLM Inference-Time Self-Improvement
presents a survey that analyzes three categories of LLM inference-time self-improvement techniques - independent methods like enhanced decoding, context-aware approaches using external data, and model collaboration strategies.
Explore Theory-of-Mind
introduces ExploreToM, a framework that uses A* search to generate diverse, complex theory-of-mind scenarios that reveal significant limitations in current LLMs' social intelligence capabilities; testing showed even advanced models like GPT-4 and Llama-3 perform poorly (as low as 5% accuracy) on these challenging scenarios, despite their strong performance on simpler benchmarks; fine-tuning on ExploreToM data improved performance on existing benchmarks by 27 points.
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.
Precise Length Control in LLMs
adapts a pre-trained decoder-only LLM to produce responses of a desired length; integrates a secondary length-difference positional encoding into the input embeddings which enables counting down to a user-set response terminal length; claims to achieve mean token errors of less than 3 tokens without compromising quality.
Phi-4 Technical Report
presents phi-4, a 14B model that surpasses its teacher model on STEM-QA capabilities. It also reports strong performance on reasoning-focused benchmarks due to improved data, training curriculum, and innovations in the post-training scheme.
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.
Does RLHF Scale?
This new paper explores the impacts of key components in the RLHF framework. Summary of main findings: 1) RLHF doesn't scale as effectively as pretraining in LLMs, with larger policy models benefiting less from RLHF when using a fixed reward model, 2) when increasing the number of responses sampled per prompt during policy training, performance improves initially but plateaus quickly, typically around 4-8 samples, 3) using larger reward models leads to better performance in reasoning tasks, but the improvements can be inconsistent across different types of tasks, and 4) increasing training data diversity for reward models is more effective than increasing response diversity per prompt, but policy training shows diminishing returns after the early stages regardless of additional data.
Reverse Thinking
shows that training LLMs to learn "reverse thinking" helps to improve performance in commonsense, math, and logical reasoning tasks. It claims to outperform a standard fine-tuning method trained on 10x more forward reasoning.
Auto-RAG
an autonomous iterative retrieval model with superior performance across many datasets; Auto-RAG is a fine-tuned LLM that leverages the decision-making capabilities of an LLM; it interacts with the retriever through multiturn dialogues, systematically planning retrievals and refining queries to acquire valuable knowledge — it performs this process until sufficient external information is obtained; the authors also show that based on question difficulty, the method can adjust the number of iterations without any human intervention.
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).
LLM Surpass Human Experts in Predicting Neuroscience Results
proposes BrainBench to study how good LLMs are at predicting experimental outcomes in neuroscience; they tuned an LLM, BrainGPT, on neuroscience literature that surpasses experts in predicting neuroscience results; report that when LLMs indicated high confidence in their predictions, their responses were more likely to be correct.
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.
Towards Open Reasoning Models for Open-Ended Solutions
proposes Marco-o1 which is a reasoning model built for open-ended solutions; Marco-o1 is powered by Chain-of-Thought (CoT) fine-tuning, Monte Carlo Tree Search (MCTS), reflection mechanisms, and more recent reasoning strategies; Marco-o1 achieves accuracy improvements of +6.17% on the MGSM (English) dataset and +5.60% on the MGSM (Chinese) dataset.
Impacts of AI on Innovation
suggests that top scientists leverage their domain knowledge to prioritize promising AI suggestions, while others waste significant resources testing false positives; finds that implementing AI materials discovery technology leads to substantial increases in productivity, with 44% more materials discovered, 39% more patent filings, and 17% more product innovation; reports that these gains came with concerning tradeoffs, as 82% of scientists reported reduced job satisfaction due to decreased creativity and skill underutilization.
Scaling Laws for Precision
introduces "precision-aware" scaling laws that predict how model performance is affected by both training and inference precision in LLMs; key findings include: 1) post-training quantization becomes more harmful as models are trained on more data, eventually making additional pretraining actively detrimental, 2) training in lower precision requires increasing model size to maintain performance, and 3) when jointly optimizing model size, data, and precision, the compute-optimal training precision is around 7-8 bits and independent of compute; also reports that when the model size is fixed, compute-optimal precision increases approximately logarithmically with data; the authors validate their predictions on models up to 1.7B parameters trained on up to 26B tokens, showing that both very high (16-bit) and very low (sub 4-bit) training precisions may be suboptimal.