From Outcomes to Processes: Guiding PRM Learning from ORM for Inference-Time Alignment (2025.acl-long)
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| Challenge: | Existing approaches to align large language models with human preferences suffer from inconsistent scoring and suboptimal alignment. |
| Approach: | They propose a dual-consistency framework that aligns partial sequences with human preferences. |
| Outcome: | The proposed framework significantly reduces granularity discrepancies and improves GPT-4 evaluation scores. |
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