TinyZero is a reproduction of DeepSeek R1 Zero in countdown and multiplication tasks. It is built upon veRL and allows the 3B base LM to develop self-verification and search abilities through reinforcement learning.
Hugging Face's initiative to replicate DeepSeek-R1, focusing on developing datasets and sharing training pipelines for reasoning models.
The article introduces Hugging Face's Open-R1 project, a community-driven initiative to reconstruct and expand upon DeepSeek-R1, a cutting-edge reasoning language model. DeepSeek-R1, which emerged as a significant breakthrough, utilizes pure reinforcement learning to enhance a base model's reasoning capabilities without human supervision. However, DeepSeek did not release the datasets, training code, or detailed hyperparameters used to create the model, leaving key aspects of its development opaque.
The Open-R1 project aims to address these gaps by systematically replicating and improving upon DeepSeek-R1's methodology. The initiative involves three main steps:
1. **Replicating the Reasoning Dataset**: Creating a reasoning dataset by distilling knowledge from DeepSeek-R1.
2. **Reconstructing the Reinforcement Learning Pipeline**: Developing a pure RL pipeline, including large-scale datasets for math, reasoning, and coding.
3. **Demonstrating Multi-Stage Training**: Showing how to transition from a base model to supervised fine-tuning (SFT) and then to RL, providing a comprehensive training framework.
The article introduces Hugging Face's Open-R1 project, a community-driven initiative to reconstruct and expand upon DeepSeek-R1, a cutting-edge reasoning language model. DeepSeek-R1, which emerged as a significant breakthrough, utilizes pure reinforcement learning to enhance a base model's reasoning capabilities without human supervision. However, DeepSeek did not release the datasets, training code, or detailed hyperparameters used to create the model, leaving key aspects of its development opaque.
The Open-R1 project aims to address these gaps by systematically replicating and improving upon DeepSeek-R1's methodology. The initiative involves three main steps:
1. **Replicating the Reasoning Dataset**: Creating a reasoning dataset by distilling knowledge from DeepSeek-R1.
2. **Reconstructing the Reinforcement Learning Pipeline**: Developing a pure RL pipeline, including large-scale datasets for math, reasoning, and coding.
3. **Demonstrating Multi-Stage Training**: Showing how to transition from a base model to supervised fine-tuning (SFT) and then to RL, providing a comprehensive training framework.
Exploring popular reinforcement learning environments in a beginner-friendly way, focusing on the Q-learning method to solve the 'Frozen Lake' environment.
An article discussing the use of Deep Q-Networks (DQNs) in reinforcement learning, which combines the principles of Q-Learning with function approximation capabilities of neural networks to address limitations of traditional Q-learning such as scalability issues and inability to handle continuous state and action spaces.
This article discusses the process of training a large language model (LLM) using reinforcement learning from human feedback (RLHF) and a new alternative method called Direct Preference Optimization (DPO). The article explains how these methods help align the LLM with human expectations and make it more efficient.
This article discusses the latest open LLM (large language model) releases, including Mixtral 8x22B, Meta AI's Llama 3, and Microsoft's Phi-3, and compares their performance on the MMLU benchmark. It also talks about Apple's OpenELM and its efficient language model family with an open-source training and inference framework. The article also explores the use of PPO and DPO algorithms for instruction finetuning and alignment in LLMs.
