Hierarchical Policy Optimization for Simultaneous Translation of Unbounded Speech (2026.acl-long)
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| Challenge: | Existing synthesis methods cannot guarantee data quality. |
| Approach: | They propose a hierarchical reward that balances translation quality and latency objectives by combining supervised fine-tuning data with supervised inputs. |
| Outcome: | The proposed model can reuse key-value caches across both modalities and eliminate redundant feature recomputation. |
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