RewardBench: the first evaluation tool for reward models.

Pytorch implementation for ManiGAN: Text-Guided Image Manipulation.

View-decoupled Transformer for Person Re-identification under Aerial-ground Camera Network (CVPR'24)

R1-Code-Interpreter: Training LLMs to Reason with Code via Supervised and Reinforcement Learning

Google Research

Automatically update arXiv papers about LLM Reasoning, LLM Evaluation, LLM & MLLM and Video Understanding using Github Actions.

Grandmaster-Level Chess Without Search

[NeurIPS 2025 Spotlight] Reasoning Environments for Reinforcement Learning with Verifiable Rewards

This repository is maintained to release dataset and models for multimodal puzzle reasoning.

CodePDE: An Inference Framework for LLM-driven PDE Solver Generation

Pytorch implementation of NIPS'23 paper: Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective

We propose a VAE-LSTM model as an unsupervised learning approach for anomaly detection in time series.

Official code, datasets and checkpoints for "Timer: Generative Pre-trained Transformers Are Large Time Series Models" (ICML 2024) and subsequent works

Parse LaTeX math expressions

Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting (NeurIPS 2019)

Official code repo for the O'Reilly Book - "Hands-On Large Language Models"

A 7B parameter model for mathematical reasoning

Search-R1: An Efficient, Scalable RL Training Framework for Reasoning & Search Engine Calling interleaved LLM based on veRL

TAT-QA (Tabular And Textual dataset for Question Answering) contains 16,552 questions associated with 2,757 hybrid contexts from real-world financial reports.

This repository contains resources for accessing the official benchmarks, codes, and checkpoints of the paper: "[**Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Ob…

Data and Code for Program of Thoughts [TMLR 2023]

[EACL 2024] ICE-Score: Instructing Large Language Models to Evaluate Code

TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.