arXiv Artificial Intelligence

PhantomEnvironments: Training LLM Agents in Fictional Worlds

1 hour ago 01.10.2026 07:00 👁 1 views
PhantomEnvironments: Training LLM Agents in Fictional Worlds

Quick summary

arXiv:2609.40221v1 Announce Type: cross Abstract: Training LLM agents with reinforcement learning (RL) is bottlenecked by environments, which must provide verifiable rewards, support long-horizon interaction, and scale cheaply. Existing approaches rely on costly human-curated data or on LLM-generated environments that risk hallucinations and benchmark contamination. We show that LLMs can instead be trained into capable search agents using synthetic environments generated entirely by rules, whose generation requires no LLM and has zero marginal cost. We build PhantomEnvironments, multi-turn RL

Key takeaways

  • arXiv:2609.40221v1 Announce Type: cross Abstract: Training LLM agents with reinforcement learning (RL) is bottlenecked by environments, which must provide verifiable rewards, support long-horizon interaction, and scale cheaply.
  • Existing approaches rely on costly human-curated data or on LLM-generated environments that risk hallucinations and benchmark contamination.
  • We show that LLMs can instead be trained into capable search agents using synthetic environments generated entirely by rules, whose generation requires no LLM and has zero marginal cost.

Why it matters

“PhantomEnvironments: Training LLM Agents in Fictional Worlds” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗