Papers by Futing Wang

2 papers
Keys to Robust Edits: From Theoretical Insights to Practical Advances (2025.acl-long)

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Challenge: Existing methods for modifying parametric memory are prone to inaccuracies due to conflicting or outdated information.
Approach: They propose a plug-and-play module that disentangles editing keys from native model representations and dynamically adjusts keys via contrastive learning to achieve robustness-specificity balance.
Outcome: The proposed method improves over robustness tests by up to 66.4% while maintaining the success rate unaffected.
Rethinking Expert Trajectory Utilization in LLM Post-training for Mathematical Reasoning (2026.acl-long)

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Challenge: evolving generic Large Language Models into specialized Large Reasoning Models requires effective post-training.
Approach: They propose a plasticity-ceiling framework to harness expert trajectories . they establish the Sequential SFT-then-RL pipeline as the superior standard .
Outcome: The proposed framework overcomes stability and premature convergence deficits in synchronized approaches.

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