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Jiashuo Wang

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3 papers

2 author rows

ICRA Conference 2025 Conference Paper

Brain-Inspired Spatial Continuous State Encoding for Efficient Spiking-Based Navigation

Qingao Chai
Jiashuo Wang
Runhao Jiang
Bo Yang
Rui Yan
Huajin Tang

Spiking neural networks (SNNs) show great potential in mapless navigation tasks due to their low power consumption, but the continuous representation of spatial information poses a challenge to SNN training. Neuroscience findings reveal that spatial cognition cells encode spatial information through population spike patterns. Inspired by this, we propose a navigation method based on SNNs, leveraging spatial cognition cells, which include grid cells (GCs), head direction cells (HDCs), and boundary vector cells (BVCs). Our method integrates spike-based information to achieve precise navigation goal encoding and egocentric environment perception, significantly improving SNN navigation capabilities in complex environments. Simulation and real-world experiments demonstrate that our method achieves significant improvements in navigation success rate and energy efficiency, showcasing superior adaptability across environments. Our work provides a novel approach to developing efficient brain-inspired navigation systems.

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NeurIPS Conference 2025 Conference Paper

LIMOPro: Reasoning Refinement for Efficient and Effective Test-time Scaling

Yang Xiao
Jiashuo WANG
Ruifeng Yuan
Chunpu Xu
Kaishuai Xu
Wenjie Li
Pengfei Liu

Large language models (LLMs) have demonstrated remarkable reasoning capabilities through test-time scaling approaches, particularly when fine-tuned with chain-of-thought (CoT) data distilled from more powerful large reasoning models (LRMs). However, these reasoning chains often contain verbose elements that mirror human problem-solving, categorized as progressive reasoning (the essential solution development path) and functional elements (verification processes, alternative solution approaches, and error corrections). While progressive reasoning is crucial, the functional elements significantly increase computational demands during test-time inference. We introduce PIR (Perplexity-based Importance Refinement), a principled framework that quantitatively evaluates the importance of each reasoning step based on its impact on answer prediction confidence. PIR systematically identifies and selectively prunes only low-importance functional steps while preserving all progressive reasoning components, creating optimized training data that maintains the integrity of the core solution path while reducing verbosity. Models fine-tuned on PIR-optimized data exhibit superior test-time scaling properties, generating more concise reasoning chains while achieving improved accuracy (+0. 9\% to +6. 6\%) with significantly reduced token usage (-3\% to -41\%) across challenging reasoning benchmarks (AIME, AMC, and GPQA Diamond). Our approach demonstrates strong generalizability across different model sizes, data sources, and token budgets, offering a practical solution for deploying reasoning-capable LLMs in scenarios where efficient test-time scaling, response time, and computational efficiency are valuable constraints. Code and dataset are available at the LIMOPro GitHub repository.

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NeurIPS Conference 2023 Conference Paper

Aligning Language Models with Human Preferences via a Bayesian Approach

Jiashuo WANG
Haozhao Wang
Shichao Sun
Wenjie Li

In the quest to advance human-centric natural language generation (NLG) systems, ensuring alignment between NLG models and human preferences is crucial. For this alignment, current popular methods leverage a reinforcement learning (RL) approach with a reward model trained on feedback from humans. However, inherent disagreements due to the subjective nature of human preferences pose a significant challenge for training the reward model, resulting in a deterioration of the NLG performance. To tackle this issue, previous approaches typically rely on majority voting or averaging to consolidate multiple inconsistent preferences into a merged one. Although straightforward to understand and execute, such methods suffer from an inability to capture the nuanced degrees of disaggregation among humans and may only represent a specialized subset of individuals, thereby lacking the ability to quantitatively disclose the universality of human preferences. To address this challenge, this paper proposes a novel approach, which employs a Bayesian framework to account for the distribution of disagreements among human preferences as training a preference model, and names it as $\textbf{d-PM}$. Besides, considering the RL strategy's inefficient and complex training process over the training efficiency, we further propose utilizing the contrastive learning strategy to train the NLG model with the preference scores derived from the d-PM model. Extensive experiments on two human-centric NLG tasks, i. e. , emotional support conversation and integrity ``Rule-of-Thumb'' generation, show that our method consistently exceeds previous SOTA models in both automatic and human evaluations.

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