arXiv:2609.39927v1 Announce Type: new Abstract: Prompt optimization improves the performance of language-model systems on downstream tasks by refining their prompts. Classical methods evaluate prompt...
The paper investigates how small lexical changes in prompts can cause large performance swings in large language models. Using a dataset of 132,000 prompt variants, the authors uncover a scaling law linking higher average task performance to lower variance and greater robustness. They identify domain-specific terminology and explicit action directives as key linguistic factors that stabilize prompts, and propose an automated Prompt-Refining Agent that reduces performance variance by 40.7% in code generation while maintaining or improving mean performance.
arXiv:2606. 11459v1 Announce Type: cross Abstract: Large Language Models are highly sensitive to prompt formulation, necessitating automatic prompt optimization to unlock their full potential.
The paper introduces Prompt-agnostic Language Models, focusing on train‑time techniques to embed prompt robustness during instruction tuning. It shows that mixing paraphrases in training causes significant gradient interference—57% of per‑paraphrasing gradients conflict—leading to the development of two gradient‑merging methods, Prompt‑TIES and Prompt‑DARE, which resolve this interference. Experiments across four models demonstrate that these methods substantially reduce per‑example variance and improve local robustness with minimal impact on overall accuracy.
arXiv:2507. 09839v2 Announce Type: replace Abstract: An increasing number of NLP applications interact with large language models (LLMs) through black-box APIs, making prompt engineering critical for controlling model behavior.
arXiv:2511. 19829v3 Announce Type: replace Abstract: Prompt optimization has become a central mechanism for eliciting strong performance from LLMs, and recent work has made substantial progress by proposing diverse prompt evaluation metrics and optimization strategies.
The paper investigates how different training strategies affect the prompt sensitivity of large language models. It reproduces and compares methods such as refined data construction and robustness objectives, finding that while robustness fine‑tuning improves over standard fine‑tuning and in‑context learning, the prompt gap remains large (40–57%). Notably, newer techniques like CoIN and PPCL often underperform a simple data‑construction approach that uses one template per batch, and diagnostics suggest that mixed‑template batches force the optimizer to reconcile conflicting updates rather than learn a prompt‑agnostic representation.
The paper investigates why prompt optimization works better for some tasks than others by decomposing reward variance into response variance and system‑prompt variance. It finds that optimization succeeds when system‑prompt variance dominates, and that adding more user prompts can actually reduce this variance, especially on heterogeneous datasets. To address this, the authors propose $p1$, a filtering method that selects a small set of high‑variance user prompts, which improves optimization on reasoning benchmarks and even allows a system prompt trained on just two AIME 24 prompts to generalize well.
The paper introduces soft‑prompt tuning as a lightweight, automated method for evaluating large language models (LLMs). By optimizing only ten soft‑prompt vectors—about 0.0006% of a 7B model’s parameters—within 150 steps, the approach adapts models to specific benchmark formats, improving format compliance while preserving knowledge accuracy. Experiments across seven models and eleven datasets show that this method matches strong evaluation‑harness baselines, enhances evaluation signals on challenging datasets, and yields cleaner outputs that align better with exact‑match scoring and independent LLM judges.
Large language model (LLM) agents require post-training methods that can improve long-horizon decision making from environment feedback. However, existing agentic post-training pipelines often treat data curation as a fixed preprocessing step, focusing mainly on data augmentation while neglecting filtering, refinement, and adaptation to downstream failures.
arXiv:2511. 19829v2 Announce Type: replace Abstract: Most prompt-optimization methods refine a single static template, making them ineffective in complex and dynamic user scenarios.
Ecdysis is a framework for training runtime harnesses for large language model agents that reduces training time and improves performance. It distinguishes between model‑specific issues and systematic harness deficiencies by aggregating failures across multiple task instances and uses Failure‑Driven Collaborative Refinement to diagnose and correct harness problems. Experiments show up to a 1.84× speedup in harness training and an 18.56% increase in reasoning accuracy.