Author

Xing-Ran Chen

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#machine learning Preprint Oct 2026

Multi-Token Random-Walk Learning via Meeting-Triggered Information Exchange

This work investigates multi-token RW learning under two information exchange policies, which average stochastic gradients and updated models, respectively, when tokens meet, and establishes iteration complexity lower bounds for non-convex, convex, and strongly convex objectives on complete graphs.

Tai-Chun Yeh, Xing-Ran Chen · 0 citations

#artificial intelligence Preprint Aug 2026

RW-LoRA: Communication-Efficient Decentralized LoRA Fine-Tuning via Random Walks

A random-walk-based LoRA fine-tuning scheme that eliminates the need for global synchronization, substantially reduces communication and computation costs, and avoids aggregation errors is proposed and provides rigorous convergence guarantees for non-convex objectives under standard assumptions.

Xing-Ran Chen, Rohit Bhagat, Ghadir Ayache et al. · 1 citation