无需外部验证器，动态分配算力提升大模型推理效率
DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling
- 融合并行与串行采样，生成连贯的推理链
- 基于多臂赌博机动态分配计算资源，提升效率
- 适合资源受限场景下的高效推理应用
推理时扩展通过增加测试时计算量有效提升了大语言模型（LLM）性能，但其实际应用常受限于对外部验证器的依赖或缺乏对真实计算约束的优化。我们提出DynScaling，通过两项核心创新克服这些限制：集成的并行-串行采样策略和基于赌徒算法的动态预算分配框架。该采样策略将初始独立的并行响应构建为合成的串行推理链，促进多样化且一致的推理轨迹。动态预算分配框架将计算资源分配建模为多臂赌博机问题，根据先前采样响应的不确定性自适应分配推理预算，从而最大化计算效率。结合这两项技术，DynScaling在不依赖外部验证器的前提下，在实际资源约束下显著提升了LLM性能。实验表明，其在任务表现和计算成本上均优于现有无验证器的推理扩展基线。
原文摘要 · Abstract (English)
Inference-time scaling has proven effective in boosting large language model (LLM) performance through increased test-time computation. Yet, its practical application is often hindered by reliance on external verifiers or a lack of optimization for realistic computational constraints. We propose DynScaling, which addresses these limitations through two primary innovations: an integrated parallel-sequential sampling strategy and a bandit-based dynamic budget allocation framework. The integrated sampling strategy unifies parallel and sequential sampling by constructing synthetic sequential reasoning chains from initially independent parallel responses, promoting diverse and coherent reasoning trajectories. The dynamic budget allocation framework formulates the allocation of computational resources as a multi-armed bandit problem, adaptively distributing the inference budget across queries based on the uncertainty of previously sampled responses, thereby maximizing computational efficiency. By combining these components, DynScaling effectively improves LLM performance under practical resource constraints without the need for external verifiers. Experimental results demonstrate that DynScaling consistently surpasses existing verifier-free inference scaling baselines in both task performance and computational cost.
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