Visual generative models have achieved remarkable progress in synthesizing photorealistic images and videos, yet aligning their outputs with human preferences across critical dimensions remains a persistent challenge. Though reinforcement learning from human feedback offers promise for preference alignment, existing reward models for visual generation face limitations, including black-box scoring without interpretability and potentially resultant unexpected biases. We present VisionReward, a general framework for learning human visual preferences in both image and video generation. Specifically, we employ a hierarchical visual assessment framework to capture fine-grained human preferences, and leverages linear weighting to enable interpretable preference learning. Furthermore, we propose a multi-dimensional consistent strategy when using VisionReward as a reward model during preference optimization for visual generation. Experiments show that VisionReward can significantly outperform existing image and video reward models on both machine metrics and human evaluation. Notably, VisionReward surpasses VideoScore by 17.2% in preference prediction accuracy, and text-to-video models with VisionReward achieve a 31.6% higher pairwise win rate compared to the same models using VideoScore.

As enterprise codebases continue to grow in scale and complexity, the volume of lint errors far exceeds engineers' manual remediation capacity, leading to continuous accumulation of technical debt and hindered development efficiency. This paper presents BitsAI-Fix, an automated lint error remediation workflow based on Large Language Models (LLMs), designed to address this critical challenge in industrial-scale environments. BitsAI-Fix employs tree-sitter for context expansion and generates search-and-replace format patches through specially trained LLMs, followed by lint scan re-verification to output final remediation results. Additionally, our approach introduces an innovative progressive reinforcement learning (RL) training strategy that can automatically acquire verifiable training data during the project cold-start phase and continuously iterate the model by collecting online samples through feedback after system deployment. Furthermore, we designed a targeted rule-based reward mechanism that combines format rewards and correctness rewards while penalizing redundant modifications. We also propose a "code diff matching" methodology to continuously track online effectiveness. In production deployment at ByteDance, our solution has supported over 5,000 engineers, resolved more than 12,000 static analysis issues, achieved approximately 85% remediation accuracy, with around 1,000 weekly active adopters. This work demonstrates the practical feasibility of LLM-based code remediation solutions in enterprise environments and serves as a reference for automated code fix in large-scale industrial scenarios.

Tianze Luo, Xingchen Miao, Wenbo Duan. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Software-Defined Networking (SDN), enabled by OpenFlow, represents a paradigm shift from traditional network to the future Internet. Compared with traditional networks, its controllers and network applications have higher requirements on the availability and scalability. This paper proposes OFBGP, a new scalable, highly available architecture of BGP totally based on distributed design, without any centralized component. OFBGP is an application of SDN controller. In the design of OFBGP, BGP functionalities are divided according to their characteristics and requirements into two modules: BGP Protocol and BGP Decision. By doing so, the application can easily scale out when the number of BGP neighbors increases, and the process of BGP routing decisions can be parallelized. The paper also presents a new solution to implement BGP NSR through snapshots and backup event flow so that from a historical snapshot, replaying real-time backup event flow can rebuild the state that has happened behind at any moment. When a fault occurs, we can easily restore a state to ensure BGP neighbors do not perceive failure. We have also built a prototype to demonstrate our architecture. After the test of the performance of some core modules, the result shows that our new architecture can help improve the availability and scalability of BGP.

Software-Defined Networking (SDN), enabled by Open Flow, represents a paradigm shift from traditional network to the future Internet. Replicate or distributed controllers have been proposed to address the issues of availability and scalability that a centralized controller suffers from. However, it lacks a flexible mechanism to balance load among distributed controllers. To address this problem, this paper presents DALB, a dynamic and adaptive algorithm for controller load balancing totally based on distributed architecture, without any centralized component. This algorithm is running as a module of SDN controller. On one hand, it adopts an adjustable load collection threshold so as to reduce the overhead of exchanging messages for load collection, and on the other hand it can make policy and election locality in order to reduce the decision delay caused by network transmission. In this paper, we build the prototype system on floodlight to demonstrate our design and test the performance of our algorithm.

Network controller is the core of the OpenFlow-Based Software-Defined Networks (SDN). High availability of network controller is an urgent need objectively. The appearance of controller instance fault should not be perceived by data plane. During the fault recovery, OpenFlow messages, especially asynchronous message from data plane, should not be discarded. Otherwise the control platform would hold the outdated network status. This is an interesting problem which we call Asynchronous Message Discard (ASMD) trouble. This paper proposes a Non-stop network controller (NSNC). It can troubleshoot the ASMD problem based on highly available TCP (HA-TCP) technology. During the fault recovery on distributed control platform, we want to elect the optimal controller to take over the network devices managed by the failed controller instance. We analyses the minimum metrics set for controller election. The view of this paper is verified on Floodlight controller. Experiments show that when the controller instance fails, asynchronous messages from switch will not be discarded, and using HA-TCP only adds negligible overhead to throughput and latency. We test the performance of election algorithm based on Mininet. Currently, there is only a theoretical analysis of metrics related to the election. We will test the validity of the election strategy in a real scenario as part of our future work.