BatchParScale: Sparse Batch-Mixing for Sequence Models
Recent test-time scaling methods improve model performance by increasing inference-time compute. One practical form of this is parallel scaling, where a model samples multiple independent reasoning streams and aggregates their answers using methods like voting, reranking, or verification. While effective, this approach leaves the streams uncoordinated, and a discovery made in one rollout cannot influence any other. Multi-Sequence Verifiers jointly score candidate answers after generation, but the reasoning streams remain independent during inference. ParScale takes a step toward model-internal parallel scaling by learning to aggregate multiple forward passes, but its streams still do not ...
| Authors | Hans Peter Lyngsøe Raaschou-jensen, M. Onurcan Kaya, Desmond Elliott |
|---|---|
| Primary category | cs.LG |
| Published | 18 October 2026 |
| Last updated at source | 18 October 2026 |
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