CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models

With the emergence of Large Language Models (LLMs), there has been a significant improvement in the programming capabilities of models, attracting growing attention from researchers. We propose CodeApex, a bilingual benchmark dataset focusing on the programming comprehension and code generation abilities of LLMs. CodeApex comprises three types of multiple-choice questions: conceptual understanding, commonsense reasoning, and multi-hop reasoning, designed to evaluate LLMs on programming comprehension tasks. Additionally, CodeApex utilizes algorithmic questions and corresponding test cases to assess the code quality generated by LLMs. We evaluate 14 state-of-the-art LLMs, including both general-purpose and specialized models. GPT exhibits the best programming capabilities, achieving approximate accuracies of 50% and 56% on the two tasks, respectively. There is still significant room for improvement in programming tasks. We hope that CodeApex can serve as a reference for evaluating the coding capabilities of LLMs, further promoting their development and growth. Datasets are released at https://github.com/APEXLAB/CodeApex.git. CodeApex submission website is https://apex.sjtu.edu.cn/codeapex/.

GFlowNets with Human Feedback

We propose the GFlowNets with Human Feedback (GFlowHF) framework to improve the exploration ability when training AI models. For tasks where the reward is unknown, we fit the reward function through human evaluations on different trajectories. The goal of GFlowHF is to learn a policy that is strictly proportional to human ratings, instead of only focusing on human favorite ratings like RLHF. Experiments show that GFlowHF can achieve better exploration ability than RLHF.

CFlowNets: Continuous Control with Generative Flow Networks

Generative flow networks (GFlowNets), as an emerging technique, can be used as an alternative to reinforcement learning for exploratory control tasks. GFlowNet aims to generate distribution proportional to the rewards over terminating states, and to sample different candidates in an active learning fashion. GFlowNets need to form a DAG and compute the flow matching loss by traversing the inflows and outflows of each node in the trajectory. No experiments have yet concluded that GFlowNets can be used to handle continuous tasks. In this paper, we propose generative continuous flow networks (CFlowNets) that can be applied to continuous control tasks. First, we present the theoretical formulation of CFlowNets. Then, a training framework for CFlowNets is proposed, including the action selection process, the flow approximation algorithm, and the continuous flow matching loss function. Afterward, we theoretically prove the error bound of the flow approximation. The error decreases rapidly as the number of flow samples increases. Finally, experimental results on continuous control tasks demonstrate the performance advantages of CFlowNets compared to many reinforcement learning methods, especially regarding exploration ability.

S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?

Collaborative multi-agent reinforcement learning (MARL) has been widely used in many practical applications, where each agent makes a decision based on its own observation. Most mainstream methods treat each local observation as an entirety when modeling the decentralized local utility functions. However, they ignore the fact that local observation information can be further divided into several entities, and only part of the entities is helpful to model inference. Moreover, the importance of different entities may change over time. To improve the performance of decentralized policies, the attention mechanism is used to capture features of local information. Nevertheless, existing attention models rely on dense fully connected graphs and cannot better perceive important states. To this end, we propose a sparse state based MARL (S2RL) framework, which utilizes a sparse attention mechanism to discard irrelevant information in local observations. The local utility functions are estimated through the self-attention and sparse attention mechanisms separately, then are combined into a standard joint value function and auxiliary joint value function in the central critic. We design the S2RL framework as a plug-and-play module, making it general enough to be applied to various methods. Extensive experiments on StarCraft II show that S2RL can significantly improve the performance of many state-of-the-art methods.

Mining Latent Relationships among Clients: Peer-to-peer Federated Learning with Adaptive Neighbor Matching

In federated learning (FL), clients may have diverse objectives, merging all clients' knowledge into one global model will cause negative transfers to local performance. Thus, clustered FL is proposed to group similar clients into clusters and maintain several global models. Nevertheless, current clustered FL algorithms require the assumption of the number of clusters, they are not effective enough to explore the latent relationships among clients. However, we take advantage of peer-to-peer (P2P) FL, where clients communicate with neighbors without a central server and propose an algorithm that enables clients to form an effective communication topology in a decentralized manner without assuming the number of clusters. Additionally, the P2P setting will release the concerns caused by the central server in centralized FL, such as reliability and communication bandwidth problems. In our method, 1) we present two novel metrics for measuring client similarity, applicable under P2P protocols; 2) we devise a two-stage algorithm, in the first stage, an efficient method to enable clients to match same-cluster neighbors with high confidence is proposed; 3) then in the second stage, a heuristic method based on Expectation Maximization under the Gaussian Mixture Model assumption of similarities is used for clients to discover more neighbors with similar objectives. We make a theoretical analysis of how our work is superior to the P2P FL counterpart and extensive experiments show that our method outperforms all P2P FL baselines and has comparable or even superior performance to centralized cluster FL. Moreover, results show that our method is much effective in mining latent cluster relationships under various heterogeneity without assuming the number of clusters and it is effective even under low communication budgets.

CATNet: Context AggregaTion Network for Instance Segmentation in Remote Sensing Images

The task of instance segmentation in remote sensing images, aiming at performing per-pixel labeling of objects at instance level, is of great importance for various civil applications. Despite previous successes, most existing instance segmentation methods designed for natural images encounter sharp performance degradations when directly applied to top-view remote sensing images. Through careful analysis, we observe that the challenges mainly come from lack of discriminative object features due to severe scale variations, low contrasts, and clustered distributions. In order to address these problems, a novel context aggregation network (CATNet) is proposed to improve the feature extraction process. The proposed model exploits three lightweight plug-and-play modules, namely dense feature pyramid network (DenseFPN), spatial context pyramid (SCP), and hierarchical region of interest extractor (HRoIE), to aggregate global visual context at feature, spatial, and instance domains, respectively. DenseFPN is a multi-scale feature propagation module that establishes more flexible information flows by adopting inter-level residual connections, cross-level dense connections, and feature re-weighting strategy. Leveraging the attention mechanism, SCP further augments the features by aggregating global spatial context into local regions. For each instance, HRoIE adaptively generates RoI features for different downstream tasks. We carry out extensive evaluation of the proposed scheme on the challenging iSAID, DIOR, NWPU VHR-10, and HRSID datasets. The evaluation results demonstrate that the proposed approach outperforms state-of-the-arts with similar computational costs. Code is available at https://github.com/yeliudev/CATNet.

Ensemble Federated Adversarial Training with Non-IID data

Despite federated learning endows distributed clients with a cooperative training mode under the premise of protecting data privacy and security, the clients are still vulnerable when encountering adversarial samples due to the lack of robustness. The adversarial samples can confuse and cheat the client models to achieve malicious purposes via injecting elaborate noise into normal input. In this paper, we introduce a novel Ensemble Federated Adversarial Training Method, termed as EFAT, that enables an efficacious and robust coupled training mechanism. Our core idea is to enhance the diversity of adversarial examples through expanding training data with different disturbances generated from other participated clients, which helps adversarial training perform well in Non-IID settings. Experimental results on different Non-IID situations, including feature distribution skew and label distribution skew, show that our proposed method achieves promising results compared with solely combining federated learning with adversarial approaches.

Transfer Heterogeneous Knowledge Among Peer-to-Peer Teammates: A Model Distillation Approach

Peer-to-peer knowledge transfer in distributed environments has emerged as a promising method since it could accelerate learning and improve team-wide performance without relying on pre-trained teachers in deep reinforcement learning. However, for traditional peer-to-peer methods such as action advising, they have encountered difficulties in how to efficiently expressed knowledge and advice. As a result, we propose a brand new solution to reuse experiences and transfer value functions among multiple students via model distillation. But it is still challenging to transfer Q-function directly since it is unstable and not bounded. To address this issue confronted with existing works, we adopt Categorical Deep Q-Network. We also describe how to design an efficient communication protocol to exploit heterogeneous knowledge among multiple distributed agents. Our proposed framework, namely Learning and Teaching Categorical Reinforcement (LTCR), shows promising performance on stabilizing and accelerating learning progress with improved team-wide reward in four typical experimental environments.