arXiv (All)
open access
Test-Time Policy Adaptation for Enhanced Multi-Turn Interactions with LLMs
Large Language Models (LLMs) employ multi-turn interaction as a fundamental paradigm for completing complex tasks. However, their performance often degrades in extended interactions, as they are typically trained on static, single-turn data, which hinders their ability to adapt to real-time user fee
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Words & Weights: Streamlining Multi-Turn Interactions via Co-Adaptation#1117333
- Resample or Reroute? Budget-Aware Test-Time Model Selection for Large Language Models#353048
- Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning#328707
- Learning an Anchored Prompt Space for Continual Adaptation of Large Language Models#1116830
- From Parameters to Behaviors: A Survey of Model Fusion for Large Language Models#1014145
Record · ID 1117250
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive