Athena: Enhancing Multimodal Reasoning with Data-efficient Process Reward Models
📰 ArXiv cs.AI
Learn how Athena-PRM enhances multimodal reasoning with data-efficient process reward models, improving performance while reducing annotation needs
Action Steps
- Implement Athena-PRM using PyTorch or TensorFlow to evaluate reward scores for each step in solving complex reasoning problems
- Use Monte Carlo estimation as a baseline for comparison with Athena-PRM
- Annotate a small subset of data with step-level annotations to fine-tune Athena-PRM
- Evaluate the performance of Athena-PRM on a multimodal reasoning task, such as visual question answering
- Compare the results of Athena-PRM with conventional automated labeling methods to assess its data efficiency and accuracy
Who Needs to Know This
Researchers and engineers working on multimodal reasoning and process reward models can benefit from Athena-PRM, as it provides a more efficient and accurate way to evaluate reward scores for complex reasoning problems
Key Insight
💡 Athena-PRM reduces the need for extensive step-level annotations, making it a more efficient and cost-effective solution for evaluating reward scores in complex reasoning problems
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Enhance multimodal reasoning with Athena-PRM, a data-efficient process reward model #multimodalreasoning #processrewardmodel
Full Article
Title: Athena: Enhancing Multimodal Reasoning with Data-efficient Process Reward Models
Abstract:
arXiv:2506.09532v5 Announce Type: replace-cross Abstract: We present Athena-PRM, a multimodal process reward model (PRM) designed to evaluate the reward score for each step in solving complex reasoning problems. Developing high-performance PRMs typically demands significant time and financial investment, primarily due to the necessity for step-level annotations of reasoning steps. Conventional automated labeling methods, such as Monte Carlo estimation, often produce noisy labels and incur substa
Abstract:
arXiv:2506.09532v5 Announce Type: replace-cross Abstract: We present Athena-PRM, a multimodal process reward model (PRM) designed to evaluate the reward score for each step in solving complex reasoning problems. Developing high-performance PRMs typically demands significant time and financial investment, primarily due to the necessity for step-level annotations of reasoning steps. Conventional automated labeling methods, such as Monte Carlo estimation, often produce noisy labels and incur substa
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