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Training Large Language Models to Reason in a Continuous Latent Space
Paper • 2412.06769 • Published • 74 -
Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
Paper • 2408.03314 • Published • 54 -
ICAL: Continual Learning of Multimodal Agents by Transforming Trajectories into Actionable Insights
Paper • 2406.14596 • Published • 5 -
A Comprehensive Survey of LLM Alignment Techniques: RLHF, RLAIF, PPO, DPO and More
Paper • 2407.16216 • Published
Collections
Discover the best community collections!
Collections including paper arxiv:2501.04519
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Search, Verify and Feedback: Towards Next Generation Post-training Paradigm of Foundation Models via Verifier Engineering
Paper • 2411.11504 • Published • 20 -
Top-nσ: Not All Logits Are You Need
Paper • 2411.07641 • Published • 20 -
Adaptive Decoding via Latent Preference Optimization
Paper • 2411.09661 • Published • 10 -
When Precision Meets Position: BFloat16 Breaks Down RoPE in Long-Context Training
Paper • 2411.13476 • Published • 15
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On Memorization of Large Language Models in Logical Reasoning
Paper • 2410.23123 • Published • 18 -
LLMs Do Not Think Step-by-step In Implicit Reasoning
Paper • 2411.15862 • Published • 8 -
Training Large Language Models to Reason in a Continuous Latent Space
Paper • 2412.06769 • Published • 74 -
Deliberation in Latent Space via Differentiable Cache Augmentation
Paper • 2412.17747 • Published • 29
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Differential Transformer
Paper • 2410.05258 • Published • 169 -
PaliGemma 2: A Family of Versatile VLMs for Transfer
Paper • 2412.03555 • Published • 124 -
VisionZip: Longer is Better but Not Necessary in Vision Language Models
Paper • 2412.04467 • Published • 105 -
o1-Coder: an o1 Replication for Coding
Paper • 2412.00154 • Published • 43
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Let's Verify Step by Step
Paper • 2305.20050 • Published • 10 -
LLM Critics Help Catch LLM Bugs
Paper • 2407.00215 • Published -
Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
Paper • 2407.21787 • Published • 12 -
Generative Verifiers: Reward Modeling as Next-Token Prediction
Paper • 2408.15240 • Published • 13
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VILA^2: VILA Augmented VILA
Paper • 2407.17453 • Published • 40 -
Octopus v4: Graph of language models
Paper • 2404.19296 • Published • 117 -
Octo-planner: On-device Language Model for Planner-Action Agents
Paper • 2406.18082 • Published • 48 -
Dolphin: Long Context as a New Modality for Energy-Efficient On-Device Language Models
Paper • 2408.15518 • Published • 43
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Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length
Paper • 2404.08801 • Published • 65 -
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models
Paper • 2404.07839 • Published • 44 -
Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence
Paper • 2404.05892 • Published • 33 -
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Paper • 2312.00752 • Published • 139
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MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
Paper • 2403.09611 • Published • 126 -
Evolutionary Optimization of Model Merging Recipes
Paper • 2403.13187 • Published • 51 -
MobileVLM V2: Faster and Stronger Baseline for Vision Language Model
Paper • 2402.03766 • Published • 14 -
LLM Agent Operating System
Paper • 2403.16971 • Published • 65
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ORPO: Monolithic Preference Optimization without Reference Model
Paper • 2403.07691 • Published • 64 -
sDPO: Don't Use Your Data All at Once
Paper • 2403.19270 • Published • 41 -
Teaching Large Language Models to Reason with Reinforcement Learning
Paper • 2403.04642 • Published • 46 -
Best Practices and Lessons Learned on Synthetic Data for Language Models
Paper • 2404.07503 • Published • 30
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Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment
Paper • 2401.12474 • Published • 35 -
LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression
Paper • 2403.12968 • Published • 25 -
RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models
Paper • 2310.00746 • Published • 1 -
LESS: Selecting Influential Data for Targeted Instruction Tuning
Paper • 2402.04333 • Published • 3