Papers by Runxi Cheng

2 papers
Spectral Characterization and Mitigation of Sequential Knowledge Editing Collapse (2026.acl-long)

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Challenge: Existing approaches to reducing the effects of knowledge editing are insufficiently understood.
Approach: They propose a plug-and-play framework that preserves the dominant subspace of the original weights and analyzes parameter updates in the spectral basis of the weights.
Outcome: The proposed framework improves editing efficacy while preserving general abilities under long-horizon sequential editing, including extreme settings with up to 20,000 edits.
HS-STaR: Hierarchical Sampling for Self-Taught Reasoners via Difficulty Estimation and Budget Reallocation (2025.emnlp-main)

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Challenge: Recent studies have incorporated reward models to guide response selection or decoding, aiming to obtain higher-quality data.
Approach: They propose a Hierarchical Sampling framework for self-taught reasoners that allocates a fixed sampling budget to problem boundary-level problems and then reallocates the remaining budget toward high-utility problems during a re-sampling phase.
Outcome: The proposed framework outperforms baseline models without additional sampling budgets across multiple reasoning benchmarks and backbone LLMs.

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