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Sicheng Li

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ICML Conference 2025 Conference Paper

DipLLM: Fine-Tuning LLM for Strategic Decision-making in Diplomacy

  • Kaixuan Xu
  • Jiajun Chai
  • Sicheng Li
  • Yuqian Fu
  • Yuanheng Zhu
  • Dongbin Zhao

Diplomacy is a complex multiplayer game that re- quires both cooperation and competition, posing significant challenges for AI systems. Traditional methods rely on equilibrium search to generate extensive game data for training, which demands substantial computational resources. Large Lan- guage Models (LLMs) offer a promising alterna- tive, leveraging pre-trained knowledge to achieve strong performance with relatively small-scale fine-tuning. However, applying LLMs to Diplo- macy remains challenging due to the exponential growth of possible action combinations and the intricate strategic interactions among players. To address this challenge, we propose DipLLM, a fine-tuned LLM-based agent that learns equilib- rium policies for Diplomacy. DipLLM employs an autoregressive factorization framework to sim- plify the complex task of multi-unit action assign- ment into a sequence of unit-level decisions. By defining an equilibrium policy within this frame- work as the learning objective, we fine-tune the model using only 1. 5% of the data required by the state-of-the-art Cicero model, surpassing its per- formance. Our results demonstrate the potential of fine-tuned LLMs for tackling complex strategic decision-making in multiplayer games.

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ICLR Conference 2025 Conference Paper

Empowering LLM Agents with Zero-Shot Optimal Decision-Making through Q-learning

  • Jiajun Chai
  • Sicheng Li
  • Yuqian Fu
  • Dongbin Zhao
  • Yuanheng Zhu

Large language models (LLMs) are trained on extensive text data to gain general comprehension capability. Current LLM agents leverage this ability to make zero- or few-shot decisions without reinforcement learning (RL) but fail in making optimal decisions, as LLMs inherently perform next-token prediction rather than maximizing rewards. In contrast, agents trained via RL could make optimal decisions but require extensive environmental interaction. In this work, we develop an algorithm that combines the zero-shot capabilities of LLMs with the optimal decision-making of RL, referred to as the Model-based LLM Agent with Q-Learning (MLAQ). MLAQ employs Q-learning to derive optimal policies from transitions within memory. However, unlike RL agents that collect data from environmental interactions, MLAQ constructs an imagination space fully based on LLM to perform imaginary interactions for deriving zero-shot policies. Our proposed UCB variant generates high-quality imaginary data through interactions with the LLM-based world model, balancing exploration and exploitation while ensuring a sub-linear regret bound. Additionally, MLAQ incorporates a mixed-examination mechanism to filter out incorrect data. We evaluate MLAQ in benchmarks that present significant challenges for existing LLM agents. Results show that MLAQ achieves a optimal rate of over 90\% in tasks where other methods struggle to succeed. Additional experiments are conducted to reach the conclusion that introducing model-based RL into LLM agents shows significant potential to improve optimal decision-making ability. Our interactive website is available at http://mlaq.site.

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AAAI Conference 2022 Conference Paper

MIA-Former: Efficient and Robust Vision Transformers via Multi-Grained Input-Adaptation

  • Zhongzhi Yu
  • Yonggan Fu
  • Sicheng Li
  • Chaojian Li
  • Yingyan Lin

Vision transformers (ViTs) have recently demonstrated great success in various computer vision tasks, motivating a tremendously increased interest in their deployment into many real-world IoT applications. However, powerful ViTs are often too computationally expensive to be fitted onto realworld resource-constrained devices, due to (1) their quadratically increased complexity with the number of input tokens and (2) their overparameterized self-attention heads and model depth. In parallel, different images are of varying complexity and their different regions can contain various levels of visual information, e. g. , a sky background is not as informative as a foreground object in object classification tasks, indicating that treating all regions/tokens equally in terms of model complexity is unnecessary while such opportunities for trimming down ViTs’ complexity have not been fully explored. To this end, we propose a Multi-grained Input- Adaptive Vision TransFormer framework dubbed MIA- Former that can input-adaptively adjust the structure of ViTs at three coarse-to-fine-grained granularities (i. e. , model depth and the number of model heads/tokens). In particular, our MIA-Former adopts a low-cost network trained with a hybrid supervised and reinforcement training method to skip unnecessary layers, heads, and tokens in an input adaptive manner, reducing the overall computational cost. Furthermore, an interesting side effect of our MIA-Former is that its resulting ViTs are naturally equipped with improved robustness against adversarial attacks over their static counterparts, because MIA-Former’s multi-grained dynamic control improves the model diversity similar to the effect of ensemble and thus increases the difficulty of adversarial attacks against all its sub-models. Extensive experiments and ablation studies validate that the proposed MIA-Former framework can (1) effectively allocate computation budgets adaptive to the difficulty of input images, achieving state-of-the-art (SOTA) accuracy-efficiency trade-offs, e. g. , 20% computation savings with the same or even a higher accuracy compared with SOTA dynamic transformer models, and (2) boost ViTs’ robustness accuracy under various adversarial attacks over their vanilla counterparts by 2. 4% and 3. 0%, respectively. Our code is available at https: //github. com/RICE-EIC/MIA-Former. Copyright c 2022, Association for the Advancement of Artificial Intelligence (www. aaai. org). All rights reserved.

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AAAI Conference 2021 Conference Paper

Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition

  • Zhan Chen
  • Sicheng Li
  • Bing Yang
  • Qinghan Li
  • Hong Liu

Graph convolutional networks have been widely used for skeleton-based action recognition due to their excellent modeling ability of non-Euclidean data. As the graph convolution is a local operation, it can only utilize the short-range joint dependencies and short-term trajectory but fails to directly model the distant joints relations and long-range temporal information that are vital to distinguishing various actions. To solve this problem, we present a multi-scale spatial graph convolution (MS-GC) module and a multi-scale temporal graph convolution (MT-GC) module to enrich the receptive field of the model in spatial and temporal dimensions. Concretely, the MS-GC and MT-GC modules decompose the corresponding local graph convolution into a set of subgraph convolution, forming a hierarchical residual architecture. Without introducing additional parameters, the features will be processed with a series of sub-graph convolutions, and each node could complete multiple spatial and temporal aggregations with its neighborhoods. The final equivalent receptive field is accordingly enlarged, which is capable of capturing both short- and long-range dependencies in spatial and temporal domains. By coupling these two modules as a basic block, we further propose a multi-scale spatial temporal graph convolutional network (MST-GCN), which stacks multiple blocks to learn effective motion representations for action recognition. The proposed MST-GCN achieves remarkable performance on three challenging benchmark datasets, NTU RGB+D, NTU-120 RGB+D and Kinetics-Skeleton, for skeleton-based action recognition.

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NeurIPS Conference 2020 Conference Paper

ShiftAddNet: A Hardware-Inspired Deep Network

  • Haoran You
  • Xiaohan Chen
  • Yongan Zhang
  • Chaojian Li
  • Sicheng Li
  • Zihao Liu
  • Zhangyang Wang
  • Yingyan Lin

Multiplication (e. g. , convolution) is arguably a cornerstone of modern deep neural networks (DNNs). However, intensive multiplications cause expensive resource costs that challenge DNNs' deployment on resource-constrained edge devices, driving several attempts for multiplication-less deep networks. This paper presented ShiftAddNet, whose main inspiration is drawn from a common practice in energy-efficient hardware implementation, that is, multiplication can be instead performed with additions and logical bit-shifts. We leverage this idea to explicitly parameterize deep networks in this way, yielding a new type of deep network that involves only bit-shift and additive weight layers. This hardware-inspired ShiftAddNet immediately leads to both energy-efficient inference and training, without compromising the expressive capacity compared to standard DNNs. The two complementary operation types (bit-shift and add) additionally enable finer-grained control of the model's learning capacity, leading to more flexible trade-off between accuracy and (training) efficiency, as well as improved robustness to quantization and pruning. We conduct extensive experiments and ablation studies, all backed up by our FPGA-based ShiftAddNet implementation and energy measurements. Compared to existing DNNs or other multiplication-less models, ShiftAddNet aggressively reduces over 80% hardware-quantified energy cost of DNNs training and inference, while offering comparable or better accuracies. Codes and pre-trained models are available at https: //github. com/RICE-EIC/ShiftAddNet.

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