让AI主播实时思考：30倍降本，响应快到1秒内
LiveThinking: Enabling Real-Time Efficient Reasoning for AI-Powered Livestreaming via Reinforcement Learning
- 用强化学习压缩大模型推理路径，提升效率
- 实测响应正确率升3.3%，帮助度涨21.8%
- 适合需要实时互动的电商直播场景
在AI驱动的电商直播中，数字人需实时响应以提升用户参与度，但高延迟的大规模推理模型（LRMs）难以胜任。我们提出LiveThinking，一种两阶段优化框架：第一阶段通过拒绝采样微调（RFT）将670B教师模型蒸馏为30B的Mixture-of-Experts（MoE）模型（激活参数3B），显著降低计算开销；第二阶段采用基于组相对策略优化（GRPO）的强化学习，以多目标奖励函数（兼顾准确性、有用性与简洁性）压缩推理路径。该方案实现30倍计算成本降低，支持亚秒级延迟。在淘宝直播的真实应用中，系统使响应准确率提升3.3%，帮助度提高21.8%。经数十万观众测试，显著提升商品交易总额（GMV），验证了其在实时交互场景中提升用户体验与商业成效的有效性。
原文摘要 · Abstract (English)
In AI-powered e-commerce livestreaming, digital avatars require real-time responses to drive engagement, a task for which high-latency Large Reasoning Models (LRMs) are ill-suited. We introduce LiveThinking, a practical two-stage optimization framework to bridge this gap. First, we address computational cost by distilling a 670B teacher LRM into a lightweight 30B Mixture-of-Experts (MoE) model (3B active) using Rejection Sampling Fine-Tuning (RFT). This reduces deployment overhead but preserves the teacher's verbose reasoning, causing latency. To solve this, our second stage employs reinforcement learning with Group Relative Policy Optimization (GRPO) to compress the model's reasoning path, guided by a multi-objective reward function balancing correctness, helpfulness, and brevity. LiveThinking achieves a 30-fold reduction in computational cost, enabling sub-second latency. In real-world application on Taobao Live, it improved response correctness by 3.3% and helpfulness by 21.8%. Tested by hundreds of thousands of viewers, our system led to a statistically significant increase in Gross Merchandise Volume (GMV), demonstrating its effectiveness in enhancing user experience and commercial performance in live, interactive settings.
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