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LoRA: Low-Rank Adaptation of Large Language Models

Edward J. Hu, Yelong Shen +6

An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train…

Spurious Rewards: Rethinking Training Signals in RLVR

Rulin Shao, Shuyue Stella Li +12

We show that reinforcement learning with verifiable rewards (RLVR) can elicit strong mathematical reasoning in certain language models even with spurious rewards that have…

Denoising Diffusion Probabilistic Models (DDPM)

Jonathan Ho, Ajay Jain, Pieter Abbeel

We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium…

Generative Adversarial Nets

Ian J. Goodfellow, Jean Pouget-Abadie +6

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data…

Attention Is All You Need — Explaining the Transformer

Ashish Vaswani, Noam Shazeer +6

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also…

s1: Simple Test-Time Scaling

Niklas Muennighoff, Zitong Yang +8

Test-time scaling is a promising new approach to language modeling that uses extra test-time compute to improve performance. Recently, OpenAI's o1 model showed this capability…

Mem0: Scalable Long-Term Memory for AI Agents

Prateek Chhikara, Dev Khant +3

Large Language Models (LLMs) have demonstrated remarkable prowess in generating contextually coherent responses, yet their fixed context windows pose fundamental challenges for…

KAN: Kolmogorov-Arnold Networks

Ziming Liu, Yixuan Wang +6

Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs…

Generative Adversarial Imitation Learning (GAIL)

Jonathan Ho, Stefano Ermon

Consider learning a policy from example expert behavior, without interaction with the expert or access to reinforcement signal. One approach is to recover the expert's cost…

Deep Learning ECG Segmentation for Diverse Arrhythmias

SkillOpt — Executive Strategy for Self-Evolving Agent Skills

Yifan Yang, Ziyang Gong +13

Agent skills today are hand-crafted, generated one-shot, or evolved through loosely controlled self-revision, none of which behaves like a deep-learning optimizer for the skill,…

Kimi k1.5: Scaling Reinforcement Learning with LLMs

Kimi Team, Angang Du +94

Language model pretraining with next token prediction has proved effective for scaling compute but is limited to the amount of available training data. Scaling reinforcement…

Inside MuZero: Mastering the World Without Knowing the Rules

Julian Schrittwieser, Ioannis Antonoglou +10

Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge…

Continuous Control with Deep Reinforcement Learning (DDPG)

Timothy P. Lillicrap, Jonathan J. Hunt +6

We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy…

The Empowerment of Science of Science by LLMs

The Entropy Mechanism of RL for Reasoning LLMs

Ganqu Cui, Yuchen Zhang +15

This paper aims to overcome a major obstacle in scaling RL for reasoning with LLMs, namely the collapse of policy entropy. Such phenomenon is consistently observed across vast RL…

Model Domain Mapping Function & Composition

Proximal Policy Optimization (PPO)

John Schulman, Filip Wolski +3

We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a…

RGSD: Rubric-Guided Self-Distillation

MohammadHossein Rezaei, Anas Mahmoud +7

Rubrics have emerged as an alternative to RLVR in open-ended domains where a single ground-truth final answer is not available. Existing rubric-based training methods rely on an…

RWKV: Reinventing RNNs for the Transformer Era

Bo Peng, Eric Alcaide +32

Transformers have revolutionized almost all natural language processing (NLP) tasks but suffer from memory and computational complexity that scales quadratically with sequence…

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RL

DeepSeek-AI, Daya Guo +198

General reasoning represents a long-standing and formidable challenge in artificial intelligence. Recent breakthroughs, exemplified by large language models (LLMs) and…

Decision Transformer: Reinforcement Learning via Sequence Modeling

Lili Chen, Kevin Lu +7

We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem. This allows us to draw upon the simplicity and scalability of the Transformer…

Retrieval-Augmented Generation (RAG)

Patrick Lewis, Ethan Perez +10

Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks.…

FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness

Tri Dao, Daniel Y. Fu +3

Transformers are slow and memory-hungry on long sequences, since the time and memory complexity of self-attention are quadratic in sequence length. Approximate attention methods…