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
SPARO (Skill, Prompt, And Routing Optimization), a framework that jointly optimizes task instructions, reusable skill blocks, and routing rules, is proposed, suggesting that effective language-program optimization depends not only on discovering useful task knowledge, but also on deciding where that knowledge should be...
Hao-Ran Shou, Hao-Yue Liu, Yun-Chen Huo et al.· 0 citations
MusicRLVR is introduced, which trains a language model with group relative policy optimisation (GRPO) on verifier rewards alone, needing no human annotation, reward model or music-domain supervised fine-tuning, and generalises to unseen property combinations, out-of-range parameters and more constraints than any traini...
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
Text-to-image (T2I) generators often fail to follow their prompts faithfully, producing wrong counts, swapped attributes, ambiguous relations, and illegible text. Prompt optimization repairs such failures by rewriting the user prompt, requiring no generator retraining, and has yielded promising results. However, existi...
Existing jailbreaks against aligned LLMs are discrete artifacts whose surface forms are easy to fingerprint and patch. We argue that the broader failure mode may lie not in any specific prompt, but in natural writing registers that safety tuning under-covers. Building on this insight, we introduce VAR, the first jailbr...
Zhongze Luo, Rui-He Shi, Zhenshuai Yin et al.· 0 citations
Cross-Modal Visual Feedback (CMVF) incorporates a failure-conditioned visual diagnosis stage, in which a stronger optimizer VLM inspects each failed image without access to predictions or labels, and an error-aware aggregation stage that compresses these observations into reusable, task-level visual blind-spot patterns...
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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