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Collections including paper arxiv:2504.04823

Scaling Laws for Native Multimodal Models Scaling Laws for Native Multimodal Models

Paper • 2504.07951 • Published Apr 10 • 29
Have we unified image generation and understanding yet? An empirical study of GPT-4o's image generation ability

Paper • 2504.08003 • Published Apr 9 • 49
SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Paper • 2504.11468 • Published Apr 10 • 30
Towards Learning to Complete Anything in Lidar

Paper • 2504.12264 • Published Apr 16 • 9

Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models

Paper • 2504.04823 • Published Apr 7 • 31
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Paper • 2210.17323 • Published Oct 31, 2022 • 8
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Paper • 2306.00978 • Published Jun 1, 2023 • 11
The case for 4-bit precision: k-bit Inference Scaling Laws

Paper • 2212.09720 • Published Dec 19, 2022 • 3

Reasoning, Thinking, RL and Test-Time Scaling

Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search

Paper • 2412.18319 • Published Dec 24, 2024 • 39
Token-Budget-Aware LLM Reasoning

Paper • 2412.18547 • Published Dec 24, 2024 • 46
Efficiently Serving LLM Reasoning Programs with Certaindex

Paper • 2412.20993 • Published Dec 30, 2024 • 37
B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners

Paper • 2412.17256 • Published Dec 23, 2024 • 47

LangModels-Advances-2025

Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models

Paper • 2504.04823 • Published Apr 7 • 31
Agentic Knowledgeable Self-awareness

Paper • 2504.03553 • Published Apr 4 • 27
SmolVLM: Redefining small and efficient multimodal models

Paper • 2504.05299 • Published Apr 7 • 200

RL+reason model

RL + Transformer = A General-Purpose Problem Solver

Paper • 2501.14176 • Published Jan 24 • 28
Towards General-Purpose Model-Free Reinforcement Learning

Paper • 2501.16142 • Published Jan 27 • 30
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training

Paper • 2501.17161 • Published Jan 28 • 123
MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization

Paper • 2412.12098 • Published Dec 16, 2024 • 4

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27, 2024 • 625
BitNet: Scaling 1-bit Transformers for Large Language Models

Paper • 2310.11453 • Published Oct 17, 2023 • 105
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models

Paper • 2404.02258 • Published Apr 2, 2024 • 107
TransformerFAM: Feedback attention is working memory

Paper • 2404.09173 • Published Apr 14, 2024 • 43

Scaling Laws for Native Multimodal Models Scaling Laws for Native Multimodal Models

Paper • 2504.07951 • Published Apr 10 • 29
Have we unified image generation and understanding yet? An empirical study of GPT-4o's image generation ability

Paper • 2504.08003 • Published Apr 9 • 49
SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Paper • 2504.11468 • Published Apr 10 • 30
Towards Learning to Complete Anything in Lidar

Paper • 2504.12264 • Published Apr 16 • 9

LangModels-Advances-2025

Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models

Paper • 2504.04823 • Published Apr 7 • 31
Agentic Knowledgeable Self-awareness

Paper • 2504.03553 • Published Apr 4 • 27
SmolVLM: Redefining small and efficient multimodal models

Paper • 2504.05299 • Published Apr 7 • 200

Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models

Paper • 2504.04823 • Published Apr 7 • 31
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Paper • 2210.17323 • Published Oct 31, 2022 • 8
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Paper • 2306.00978 • Published Jun 1, 2023 • 11
The case for 4-bit precision: k-bit Inference Scaling Laws

Paper • 2212.09720 • Published Dec 19, 2022 • 3

RL+reason model

RL + Transformer = A General-Purpose Problem Solver

Paper • 2501.14176 • Published Jan 24 • 28
Towards General-Purpose Model-Free Reinforcement Learning

Paper • 2501.16142 • Published Jan 27 • 30
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training

Paper • 2501.17161 • Published Jan 28 • 123
MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization

Paper • 2412.12098 • Published Dec 16, 2024 • 4

Reasoning, Thinking, RL and Test-Time Scaling

Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search

Paper • 2412.18319 • Published Dec 24, 2024 • 39
Token-Budget-Aware LLM Reasoning

Paper • 2412.18547 • Published Dec 24, 2024 • 46
Efficiently Serving LLM Reasoning Programs with Certaindex

Paper • 2412.20993 • Published Dec 30, 2024 • 37
B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners

Paper • 2412.17256 • Published Dec 23, 2024 • 47

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27, 2024 • 625
BitNet: Scaling 1-bit Transformers for Large Language Models

Paper • 2310.11453 • Published Oct 17, 2023 • 105
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models

Paper • 2404.02258 • Published Apr 2, 2024 • 107
TransformerFAM: Feedback attention is working memory

Paper • 2404.09173 • Published Apr 14, 2024 • 43

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