Papers by Shichao Ma

2 papers
TSPO: Breaking the Double Homogenization Dilemma in Multi-turn Search Policy Optimization (2026.findings-acl)

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Challenge: Large Language Models (LLMs) can solve complex tasks through iterative information retrieval.
Approach: They propose a turn-level stage-aware policy optimization approach to solve this problem . they introduce a first-occurrence latent reward mechanism to allocate partial rewards .
Outcome: Experiments show that TSPO outperforms state-of-the-art models on Qwen2.5-3B and 7B models.
PibE-MPP: A Play-it-by-Ear Masking Performance Plug-in for LLMs (2026.findings-acl)

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Challenge: Random masking is a widely adopted classic baseline in large language models (LLMs).
Approach: They propose a play-it-by-ear masking performance plug-in which enables LLMs to adaptively select masking target combinations for each task.
Outcome: The proposed performance plug-in retains the advantages and mitigates the drawbacks of random masking in large language models.

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