Establish the generality of the accuracy--consistency--coverage trade-off and PTGS beyond text-only LLMs

Establish whether the accuracy-consistency-coverage trilemma observed for text-based large language model agents persists in post-training beyond pure large language models, including multimodal large language models and vision-language-action models, and determine whether the benefits of posterior-tempered group sampling transfer to those settings.

Background

The paper studies text-based LLM agents and proposes posterior-tempered group sampling (PTGS), which adapts rollout temperature to estimated prompt difficulty during reinforcement-learning training. In the reported experiments, PTGS improves single-shot accuracy and repeated-sampling coverage while reducing the Sharpening Tax, thereby mitigating the tension among accuracy, consistency, and solution coverage.

The scope of the experiments does not include multimodal LLMs or vision-language-action models. The authors therefore explicitly identify as unresolved whether the same trilemma applies in these broader post-training settings and whether PTGS retains its benefits when agents process modalities beyond text or interact through embodied actions.