Automatic prompt optimization (APO) has been widely employed to adapt large language models without updating their weights, yielding promising results. However, existing methods such as GEPA and OPRO assume hundreds to thousands of subject-model calls, far more than is practical behind paid, rate-limited APIs. Under ti...
Hao-Yue Liu, Zhi-Chao Wang, Huan-Yu Yan et al.· 0 citations
On-policy distillation (OPD) improves the reasoning capabilities of small language models through token-level teacher supervision on student-generated trajectories. Yet can teachers that excel at solving problems independently also guide student reasoning effectively? Prior work shows that when student prefixes follow...
Xiao-Yu Ma, Hao-Yue Liu, Zhi-Chao Wang et al.· 0 citations
Automatic prompt optimization (APO) has been widely employed to adapt large language models without updating their weights, yielding promising results. However, existing methods such as GEPA and OPRO assume hundreds to thousands of subject-model calls, far more than is practical behind paid, rate-limited APIs. Under ti...
Hao-Yue Liu, Zhi-Chao Wang, Huan-Yu Yan et al.· 0 citations
Reinforcement learning over a frozen reasoner has become a common recipe for teaching a policy which external tools to invoke. We show that this recipe becomes structurally mismatched in specialist scientific settings where the complete tool-subset space is enumerable. There, a small set of recurring computational capa...
Hao-Yue Liu, Xiao-Yu Ma, Ye-Heng Chen et al.· 0 citations
This paper introduces SEPO (Structural, Evidence-grounded Prompt Optimization), a multi-trajectory prompt optimiser centred on edit-effect lineage feedback that makes prompt optimisation addressable, attributable, and actionable.
Xiao-Yu Ma, Hao-Yue Liu, Yiwen Li et al.· 1 citation
HN-CLIP is introduced, which uses the text encoder's own text-text geometry to construct per-negative adaptive similarity margins, and improves all six tested fine-tuning frameworks on the in-domain benchmarks and reaches the strongest full-data baseline with only 20% of the training data.
Hao-Yue Liu, Ye-Heng Chen, Zhi-Chao Wang et al.· 0 citations
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