Cross-Environment Neural Reranking for Sample-Efficient Action Selection in Text-Based Agents
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 substantially improves over single-environment ALFWorld performance (net gain +0.412) while maintaining competitive WebShop performance (+0.214 vs...
| Authors | Kan Shao |
|---|---|
| Primary category | cs.CL |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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