training data
Training data refers to the dataset used to train an AI model. It consists of labeled examples that the model learns from, allowing it to make predictions on unseen examples later.
- Benign Overfitting in Single-Head Attention
- CPRet: A Dataset, Benchmark, and Model for Retrieval in Competitive Programming
- CoT Information: Improved Sample Complexity under Chain-of-Thought Supervision
- Competitive Advantage Attacks to Decentralized Federated Learning
- EuroSpeech: A Multilingual Speech Corpus
- Explainable Reinforcement Learning from Human Feedback to Improve Alignment
- Explaining and Mitigating Crosslingual Tokenizer Inequities
- From Specificity to Generality: Revisiting Generalizable Artifacts in Detecting Face Deepfakes
- Hyper-Modality Enhancement for Multimodal Sentiment Analysis with Missing Modalities
- Improve Temporal Reasoning in Multimodal Large Language Models via Video Contrastive Decoding
- Is Limited Participant Diversity Impeding EEG-based Machine Learning?
- Is Your Diffusion Model Actually Denoising?
- JAMUN: Bridging Smoothed Molecular Dynamics and Score-Based Learning for Conformational Ensemble Generation
- LayerIF: Estimating Layer Quality for Large Language Models using Influence Functions
- LithoSim: A Large, Holistic Lithography Simulation Benchmark for AI-Driven Semiconductor Manufacturing
- Memory Mosaics at scale
- MultiScale Contextual Bandits for Long Term Objectives
- On the Edge of Memorization in Diffusion Models
- PoE-World: Compositional World Modeling with Products of Programmatic Experts
- QiMeng-SALV: Signal-Aware Learning for Verilog Code Generation
- Reasoning Gym: Reasoning Environments for Reinforcement Learning with Verifiable Rewards
- Reinforcement Learning Finetunes Small Subnetworks in Large Language Models
- SWE-smith: Scaling Data for Software Engineering Agents
- Solver-Informed RL: Grounding Large Language Models for Authentic Optimization Modeling
- The Implicit Bias of Structured State Space Models Can Be Poisoned With Clean Labels
- The Promise of RL for Autoregressive Image Editing
- The Unreasonable Effectiveness of Entropy Minimization in LLM Reasoning
- Training Language Models to Reason Efficiently
- VLMs can Aggregate Scattered Training Patches
- Zero-Shot Context Generalization in Reinforcement Learning from Few Training Contexts