Add pipeline_tag to model card
#1
by
nielsr
HF Staff
- opened
README.md
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license: mit
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datasets:
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- HannahRoseKirk/prism-alignment
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base_model:
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- Skywork/Skywork-Reward-V2-Llama-3.1-8B
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---
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# Meta Reward Modeling (MRM)
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## Overview
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**Meta Reward Modeling (MRM)** is a personalized reward modeling framework designed to adapt to diverse user preferences with limited feedback.
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Instead of learning a single global reward function, MRM treats each user as a separate learning task and applies a meta-learning approach to learn a shared initialization that enables fast, few-shot personalization.
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To improve robustness across heterogeneous users, MRM introduces a **Robust Personalization Objective (RPO)** that emphasizes hard-to-learn users during meta-training.
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## License
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This model is released under the **MIT License**.
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---
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base_model:
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- Skywork/Skywork-Reward-V2-Llama-3.1-8B
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datasets:
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- HannahRoseKirk/prism-alignment
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license: mit
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pipeline_tag: text-classification
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# Meta Reward Modeling (MRM)
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## Overview
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**Meta Reward Modeling (MRM)** is a personalized reward modeling framework designed to adapt to diverse user preferences with limited feedback. This repository provides trained checkpoints as described in the paper [One Adapts to Any: Meta Reward Modeling for Personalized LLM Alignment](https://huggingface.co/papers/2601.18731).
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Instead of learning a single global reward function, MRM treats each user as a separate learning task and applies a meta-learning approach to learn a shared initialization that enables fast, few-shot personalization.
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MRM represents user-specific rewards as adaptive combinations over shared base reward functions and optimizes this structure through a bi-level meta-learning framework. To improve robustness across heterogeneous users, MRM introduces a **Robust Personalization Objective (RPO)** that emphasizes hard-to-learn users during meta-training.
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---
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## License
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This model is released under the **MIT License**.
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