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Collections including paper arxiv:2310.00533

Papers - Training

SELF: Language-Driven Self-Evolution for Large Language Model

Paper • 2310.00533 • Published Oct 1, 2023 • 2
GrowLength: Accelerating LLMs Pretraining by Progressively Growing Training Length

Paper • 2310.00576 • Published Oct 1, 2023 • 2
A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity

Paper • 2305.13169 • Published May 22, 2023 • 3
Transformers Can Achieve Length Generalization But Not Robustly

Paper • 2402.09371 • Published Feb 14, 2024 • 15

Papers - AI - Self-refinement - Training and Tuning

SELF: Language-Driven Self-Evolution for Large Language Model

Paper • 2310.00533 • Published Oct 1, 2023 • 2
Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping

Paper • 2402.14083 • Published Feb 21, 2024 • 48

Papers - Reinforcement Learning

Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning

Paper • 2310.20587 • Published Oct 31, 2023 • 18
SELF: Language-Driven Self-Evolution for Large Language Model

Paper • 2310.00533 • Published Oct 1, 2023 • 2
Bigger, Better, Faster: Human-level Atari with human-level efficiency

Paper • 2305.19452 • Published May 30, 2023 • 4
DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search

Paper • 2408.08152 • Published Aug 15, 2024 • 56

Papers - Fine-tuning

Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning

Paper • 2310.20587 • Published Oct 31, 2023 • 18
SELF: Language-Driven Self-Evolution for Large Language Model

Paper • 2310.00533 • Published Oct 1, 2023 • 2
QLoRA: Efficient Finetuning of Quantized LLMs

Paper • 2305.14314 • Published May 23, 2023 • 50
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models

Paper • 2309.14717 • Published Sep 26, 2023 • 44

Papers - Training Research

Measuring the Effects of Data Parallelism on Neural Network Training

Paper • 1811.03600 • Published Nov 8, 2018 • 2
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost

Paper • 1804.04235 • Published Apr 11, 2018 • 2
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Paper • 1905.11946 • Published May 28, 2019 • 3
Yi: Open Foundation Models by 01.AI

Paper • 2403.04652 • Published Mar 7, 2024 • 63

Ada-Instruct: Adapting Instruction Generators for Complex Reasoning

Paper • 2310.04484 • Published Oct 6, 2023 • 5
Diversity of Thought Improves Reasoning Abilities of Large Language Models

Paper • 2310.07088 • Published Oct 11, 2023 • 5
Adapting Large Language Models via Reading Comprehension

Paper • 2309.09530 • Published Sep 18, 2023 • 77
Democratizing Reasoning Ability: Tailored Learning from Large Language Model

Paper • 2310.13332 • Published Oct 20, 2023 • 15

Branch-Solve-Merge Improves Large Language Model Evaluation and Generation

Paper • 2310.15123 • Published Oct 23, 2023 • 8
Diversity of Thought Improves Reasoning Abilities of Large Language Models

Paper • 2310.07088 • Published Oct 11, 2023 • 5
ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search

Paper • 2310.13227 • Published Oct 20, 2023 • 13
Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models

Paper • 2310.04406 • Published Oct 6, 2023 • 9

Moral Foundations of Large Language Models

Paper • 2310.15337 • Published Oct 23, 2023 • 1
Specific versus General Principles for Constitutional AI

Paper • 2310.13798 • Published Oct 20, 2023 • 3
Contrastive Prefence Learning: Learning from Human Feedback without RL

Paper • 2310.13639 • Published Oct 20, 2023 • 25
RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback

Paper • 2309.00267 • Published Sep 1, 2023 • 48

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