Cross-Environment Neural Reranking for Sample-Efficient Action Selection in Text-Based Agents
arXiv:2606.02204v1 Announce Type: new Abstract: Large language model agents achieve strong performance on text-based benchmarks but incur prohibitive inference costs, motivating the use of compact neural rerankers for action selection. We investigate whether a single lightweight model can perform action selection across multiple diverse environments, a capability that would eliminate per-environment model maintenance. Training DeBERTa-v3 (184M-434M parameters) jointly on ALFWorld, WebShop, and ScienceWorld with minority-class upsampling, we find that rebalanced two-environment joint training s
The proliferation of complex LLM agents has made their inference costs a significant bottleneck, pushing researchers to explore more efficient action selection mechanisms.
Efficient, compact neural rerankers capable of operating across diverse environments lower the operational costs and expand the applicability of sophisticated AI agents, making them more commercially viable and scalable.
The development of cross-environment rerankers could significantly reduce the per-environment model maintenance burden for AI agents, allowing for more generalized and cost-effective deployment.
- · AI Agent Developers
- · Cloud Computing Providers (reduced inference load)
- · Enterprises adopting AI agents
- · Edge AI computing
- · Companies with high-cost, single-environment AI agent deployments
- · Inefficient LLM architectures
This research directly lowers the operational cost and improves the efficiency of AI agents.
Reduced operational costs could accelerate the adoption and deployment of AI agents across various industries, leading to increased automation.
Widespread, cost-effective AI agents might fundamentally alter various white-collar workflows, potentially leading to job displacement and new service industries.
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Read at arXiv cs.CL