arXiv subjects

Rengrong Xiong

Publications and source records attributed to Rengrong Xiong.

3 recordsLinked to original sources

arXiv2026

MorphSched: Workload-Aware Adaptive Scheduler Generation for Linux

Modern Linux scheduling increasingly requires workload-specific policies to meet the dynamic performance needs of heterogeneous applications. The recent sched_ext framework enables custom BPF schedulers, but building practical policies still requires coordinating Rust user-space control logic, BPF scheduling callbacks, generated user-kernel interfaces, and strict eBPF verifier constraints. This paper presents MorphSched, an LLM-driven system for synthesizing complete Rust+BPF sched_ext schedulers. MorphSched profiles target workloads under Linux EEVDF to construct dynamic, workload-specific optimization objectives, and uses a scaffold-constrained generation pipeline to translate scheduling intent into deployable scheduler implementations. To improve synthesis reliability, MorphSched separates implementation repair from strategy search: verifier, build, and runtime failures are handled through a DAG-structured repair process, while performance feedback is accumulated in an evolutionary memory pool to refine high-level scheduling strategies across iterations. We evaluate MorphSched across 120 machine/workload/interferer configurations on a real Linux kernel. The generated schedulers compile, pass eBPF verification, load successfully, and remain stable throughout our benchmark runs without manual edits to generated scheduler source code. Across these configurations, MorphSched improves the primary foreground metric over EEVDF by 26.5% on average and up to 87.3%. In selected comparisons, the generated schedulers are also competitive with representative hand-written sched_ext schedulers. These results show that scaffold-constrained, system-grounded LLM generation can be a practical tool for exploring workload-specific Linux scheduling policies.

cs.OS↗

arXiv2026

Beyond Imitation: Filtering On-Policy Distillation by Reasoning Progress

On-policy distillation (OPD) has emerged as an effective framework for post-training language models by pairing student-generated trajectories with dense token-level supervision from a teacher. However, OPD implicitly assumes that teacher-derived rewards are an appropriate proxy for reasoning progress, and therefore treats all teacher feedback equally during policy optimization. While in practice, this assumption does not always hold. We observe that teacher-derived rewards often conflict with genuine reasoning progress, as reasoning steps with clear reasoning advancement may still receive lower distillation rewards, simply due to deviation from teacher's outputs. To address this mismatch, we propose Reasoning-Progress-Aware Reward Filtering for On-Policy Distillation (R2-OPD), which constructs two within-trajectory rankings of reasoning spans, one from teacher-derived rewards and the other from independently estimated progress reward. Distillation rewards are selectively suppressed whenever the two rankings disagree, reducing supervision that conflicts with reasoning progress while preserving effective teacher guidance. Our approach shows consistent improvement over standard OPD especially regarding reasoning performances.

cs.AI↗

arXiv2025

BPI: A Novel Efficient and Reliable Search Structure for Hybrid Storage Blockchain

Hybrid storage solutions have emerged as potent strategies to alleviate the data storage bottlenecks prevalent in blockchain systems. These solutions harness off-chain Storage Services Providers (SPs) in conjunction with Authenticated Data Structures (ADS) to ensure data integrity and accuracy. Despite these advancements, the reliance on centralized SPs raises concerns about query correctness. Although ADS can verify the existence of individual query results, they fall short of preventing SPs from omitting valid results. In this paper, we delineate the fundamental distinctions between data search in blockchains and traditional database systems. Drawing upon these insights, we introduce BPI, a lightweight framework that enables efficient keyword queries and maintenance with low overhead. We propose "Articulated Search", a query pattern specifically designed for blockchain environments that enhances search efficiency while significantly reducing costs during data user updates. Furthermore, BPI employs a suite of validation models to ensure the inclusion of all valid content in search results while maintaining low overhead. Extensive experimental evaluations demonstrate that the BPI framework achieves outstanding scalability and performance in keyword searches within blockchain, surpassing EthMB+ and state of the art search databases commonly used in mainstream hybrid storage blockchains (HSB).

cs.DB↗