arXiv Artificial Intelligence

DORA Explorer: Improving the Exploration Ability of LLMs Without Training

1 hour ago 13.08.2026 07:00 👁 1 views
DORA Explorer: Improving the Exploration Ability of LLMs Without Training

Quick summary

arXiv:2604.17244v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents for sequential decision-making struggle to produce diverse outputs. This leads to insufficient exploration, suboptimal solutions, and repeated actions. Actions are generated at the sequence level, but existing sampling strategies, such as temperature scaling, introduce diversity at the token level, not at the sequence level. We introduce DORA EXPLORER (Diversity-Oriented Ranking of Actions), a training-free, inference-time algorithm for improving exploration in LLM agents. DORA generates multiple candid

Key takeaways

  • arXiv:2604.17244v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents for sequential decision-making struggle to produce diverse outputs.
  • This leads to insufficient exploration, suboptimal solutions, and repeated actions.
  • Actions are generated at the sequence level, but existing sampling strategies, such as temperature scaling, introduce diversity at the token level, not at the sequence level.

Why it matters

“DORA Explorer: Improving the Exploration Ability of LLMs Without Training” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗