Text only partially determines prosodic realization, leaving residual variation that may align with speaker-level metadata such as regional accent or first-language (L1) background. However, because these labels are assigned to speakers, their predictive gains may overlap with broader speaker-associated structure rather than uniquely reflect category-level prosodic effects. We frame this as an attribution problem and propose a speaker-controlled audit using matched text-residual prosody predictors that differ only in access to metadata labels and speaker identity. The audit measures label utility beyond text, speaker utility beyond text, and the additional empirical utility of the label when speaker conditioning is already available. Across VCTK regional-accent labels and L2-ARCTIC L1 labels, metadata yields modest gains over text, while speaker conditioning yields larger gains and leaves little additional label utility under speaker control. Random speaker-group and label-perturbation controls further characterize the specificity and use of the supplied metadata. Importantly, speaker-held-out evaluation shows that metadata transfer to unseen speakers is limited and target-dependent, indicating that observed-speaker metadata gains do not by themselves establish speaker-independent category-level effects. These results position speaker-controlled auditing as a prerequisite attribution check before assigning category-level interpretations to metadata-conditioned predictive gains.

Finding appropriate prompts for the specific task has become an important issue as the usage of Large Language Models (LLM) have expanded. However, the variety of input-output formats complicate finding the prompts. Reinforcement Learning (RL) is a promising for prompt tuning due to its ability to incrementally produce better results through interaction with the environment. But its inherent training instability and environmental dependency make it difficult to use in practice. In this paper, we propose StablePrompt, a prompt tuning method based on RL. We formulate prompt tuning as RL problem between agent and target LLM, and introduce Adaptive Proximal Policy Optimization (APPO), an modified version of PPO for prompt tuning. APPO introduces an anchor model and updates it adaptively based on the training trajectory. Using this anchor model for the KL divergence term in PPO keeps the search space flexible and ensures training stability. We evaluate StablePrompt on various tasks, including text classification, question answering, and text generation. StablePrompt achieves State-of-The-Art performance across diverse tasks. We demonstrates that StablePrompt performs well across various types and sizes of LLMs. Furthermore, we present TTE-StablePrompt, an extension for generating input-dependent prompts. It outperforms StablePrompt in tasks that are hard to solve with a single prompt. This shows that StablePrompt is an extensible and stable RL framework for LLM.