Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning
Paper
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2310.20587
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Published
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16
Papers - Fine-tuning
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SELF: Language-Driven Self-Evolution for Large Language Model
Paper • 2310.00533 • Published • 2 -
QLoRA: Efficient Finetuning of Quantized LLMs
Paper • 2305.14314 • Published • 45 -
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Paper • 2309.14717 • Published • 44 -
Table-GPT: Table-tuned GPT for Diverse Table Tasks
Paper • 2310.09263 • Published • 39 -
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
Paper • 2401.01335 • Published • 64 -
LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement
Paper • 2403.15042 • Published • 25 -
Toolformer: Language Models Can Teach Themselves to Use Tools
Paper • 2302.04761 • Published • 11 -
The Unreasonable Ineffectiveness of the Deeper Layers
Paper • 2403.17887 • Published • 78 -
InternLM2 Technical Report
Paper • 2403.17297 • Published • 29 -
LIMA: Less Is More for Alignment
Paper • 2305.11206 • Published • 21 -
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Paper • 2305.18290 • Published • 48 -
sDPO: Don't Use Your Data All at Once
Paper • 2403.19270 • Published • 39 -
Deep reinforcement learning from human preferences
Paper • 1706.03741 • Published • 3 -
Fine-tuning Language Models for Factuality
Paper • 2311.08401 • Published • 28 -
An Emulator for Fine-Tuning Large Language Models using Small Language Models
Paper • 2310.12962 • Published • 14 -
Gecko: Versatile Text Embeddings Distilled from Large Language Models
Paper • 2403.20327 • Published • 47 -
Model Stock: All we need is just a few fine-tuned models
Paper • 2403.19522 • Published • 10 -
ReFT: Representation Finetuning for Language Models
Paper • 2404.03592 • Published • 89 -
UltraFeedback: Boosting Language Models with High-quality Feedback
Paper • 2310.01377 • Published • 5 -
RL for Consistency Models: Faster Reward Guided Text-to-Image Generation
Paper • 2404.03673 • Published • 14 -
Stream of Search (SoS): Learning to Search in Language
Paper • 2404.03683 • Published • 23 -
CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialogues
Paper • 2404.03820 • Published • 24 -
ORPO: Monolithic Preference Optimization without Reference Model
Paper • 2403.07691 • Published • 62 -
Learn Your Reference Model for Real Good Alignment
Paper • 2404.09656 • Published • 82 -
Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking
Paper • 2402.14811 • Published • 4 -
Comprehensive Survey of Model Compression and Speed up for Vision Transformers
Paper • 2404.10407 • Published • 1 -
OpenBezoar: Small, Cost-Effective and Open Models Trained on Mixes of Instruction Data
Paper • 2404.12195 • Published • 11 -
Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning
Paper • 2303.15647 • Published • 4 -
Hyper-X: A Unified Hypernetwork for Multi-Task Multilingual Transfer
Paper • 2205.12148 • Published • 2 -
Beyond the Turn-Based Game: Enabling Real-Time Conversations with Duplex Models
Paper • 2406.15718 • Published • 14 -
In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering
Paper • 2311.06668 • Published • 5 -
SpreadsheetLLM: Encoding Spreadsheets for Large Language Models
Paper • 2407.09025 • Published • 128 -
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper • 2403.13372 • Published • 62 -
Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation
Paper • 2411.00412 • Published • 9 -
CLEAR: Character Unlearning in Textual and Visual Modalities
Paper • 2410.18057 • Published • 197 -
LoRA vs Full Fine-tuning: An Illusion of Equivalence
Paper • 2410.21228 • Published • 1