Reinforcement learning has become a powerful approach for enhancing large language model reasoning, but faces a fundamental dilemma: training on easy problems can cause overfitting and pass@k degradation, while training on hard problems often results in sparse rewards.Recent question augmentation methods address this by prepending partial solutions as hints.However, uniform hint provision may introduce redundant information while missing critical reasoning bottlenecks, and excessive hints can reduce reasoning diversity, causing pass@k degradation.We propose PieceHint, a hint injection framework that strategically identifies and provides critical reasoning steps during training.By scoring the importance of different reasoning steps, selectively allocating hints based on problem difficulty, and progressively withdrawing scaffolding, PieceHint enables models to transition from guided learning to independent reasoning.Experiments on six mathematical reasoning benchmarks show that our 1.5B model achieves comparable average performance to 32B baselines while preserving pass@k diversity across all k values.

Xiaoliang Fu, Jiaye Lin, Yangyi Fang, Chaowen Hu, Cong Qin, Zekai Shao, Binbin Zheng, Lu Pan, Ke Zeng. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Xiaoliang Fu, Jiaye Lin, Yangyi Fang, Binbin Zheng, Chaowen Hu, Zekai Shao, Cong Qin, Lu Pan, Ke Zeng, Xunliang Cai. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Shared-account usage is common on short-video platforms, especially on mobile and tablet devices, where a single device is accessed by multiple users. While existing industrial solutions generally focus on behavior sequence purification to disentangle mixed user preferences, such approaches inherently depend on behavior accumulation and therefore lack the capability for real-time user identification. To adapt to online recommendation, utilizing gesture interaction features is a natural and promising option, as they (1) are instantaneous without behavior collection and (2) naturally encode fine-grained user operation habits. Nevertheless, we empirically observe that directly incorporating raw gesture features into recommendation models yields limited gains. Identity-discriminative patterns embedded in gesture signals are largely entangled during the main model training, preventing them from being leveraged as explicit and reliable identity cues. As a result, efficiently utilizing gesture information to provide more distinct identity signals for recommendation models remains a critical challenge. To address this issue, we propose G-CORE (Gesture Clustering for Real-time REcommendation), an unsupervised framework that disentangles gesture representations via clustering before integrating them into the main recommendation model. By providing clearer and more identity-aware signals, G-CORE enables the main model with faster user switching without relying on a volume of behavior accumulation. Through extensive offline experiments and online A/B tests on Kuaishou platform, G-CORE demonstrates its effectiveness in various shared-account scenarios, and has been successfully deployed in the Mobile and Tablet system of the platform.