mjf-su/FaithfulnessGuidanceReward
The mjf-su/FaithfulnessGuidanceReward is a 4 billion parameter language model fine-tuned from mjf-su/PhysicalAI-reason-VLA-MetaAction-1e, utilizing the TRL framework. This model is specifically trained with GRPO (Guidance-based Reinforcement Learning for Policy Optimization), a method designed to enhance mathematical reasoning and faithfulness in language models, as introduced in the DeepSeekMath paper. With a context length of 32768 tokens, it is optimized for tasks requiring robust and faithful reasoning capabilities.
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Model Overview
The mjf-su/FaithfulnessGuidanceReward is a 4 billion parameter language model, fine-tuned from the mjf-su/PhysicalAI-reason-VLA-MetaAction-1e base model. It leverages the TRL (Transformer Reinforcement Learning) framework for its training process.
Key Capabilities & Training
This model's core differentiator lies in its training methodology: it was developed using GRPO (Guidance-based Reinforcement Learning for Policy Optimization). This technique, detailed in the research paper "DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models" (arXiv:2402.03300), aims to significantly improve the model's mathematical reasoning and faithfulness in its responses. The training process can be visualized via Weights & Biases, indicating a focus on robust and reliable output generation.
Technical Specifications
- Parameters: 4 Billion
- Context Length: 32768 tokens
- Frameworks: TRL (0.26.1), Transformers (4.57.6), Pytorch (2.10.0), Datasets (4.4.1), Tokenizers (0.22.1)
When to Use This Model
This model is particularly well-suited for applications where:
- Faithful and accurate reasoning is paramount.
- Tasks involve mathematical problem-solving or require logical consistency.
- You need a model that has been specifically optimized for guidance-based reinforcement learning to produce more reliable outputs.