Trending Research
s1: Simple test-time scaling
simplescaling/s1
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After supervised finetuning the Qwen2. 5-32B-Instruct language model on s1K and equipping it with budget forcing, our model s1-32B exceeds o1-preview on competition math questions by up to 27% (MATH and AIME24).
Ranked #4 on
Mathematical Reasoning
on AIME24
Data Formulator 2: Iteratively Creating Rich Visualizations with AI
To create rich visualizations, data analysts often need to iterate back and forth among data processing and chart specification to achieve their goals.
LLM4Decompile: Decompiling Binary Code with Large Language Models
Decompilation aims to convert binary code to high-level source code, but traditional tools like Ghidra often produce results that are difficult to read and execute.
Cut Your Losses in Large-Vocabulary Language Models
We implement a custom kernel that performs the matrix multiplications and the log-sum-exp reduction over the vocabulary in flash memory, making global memory consumption for the cross-entropy computation negligible.
DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding
We present DeepSeek-VL2, an advanced series of large Mixture-of-Experts (MoE) Vision-Language Models that significantly improves upon its predecessor, DeepSeek-VL, through two key major upgrades.
Ranked #1 on
Referring Expression Comprehension
on RefCOCOg-test
Flaming-hot Initiation with Regular Execution Sampling for Large Language Models
volcengine/verl
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Since the release of ChatGPT, large language models (LLMs) have demonstrated remarkable capabilities across various domains.
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
We present rStar-Math to demonstrate that small language models (SLMs) can rival or even surpass the math reasoning capability of OpenAI o1, without distillation from superior models.
Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback
pku-alignment/align-anything
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In this work, we make the first attempt to fine-tune all-modality models (i. e. input and output with any modality, also named any-to-any models) using human preference data across all modalities (including text, image, audio, and video), ensuring its behavior aligns with human intentions.
FireRedASR: Open-Source Industrial-Grade Mandarin Speech Recognition Models from Encoder-Decoder to LLM Integration
We present FireRedASR, a family of large-scale automatic speech recognition (ASR) models for Mandarin, designed to meet diverse requirements in superior performance and optimal efficiency across various applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1.
Ranked #1 on
Mathematical Reasoning
on AIME24
