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Requesting Expert Reasoning: Augmenting LLM Agents with Learned Collaborative Intervention

Zhiming Wang, Jinwei He, Feng Lu

TL;DR

It is demonstrated that successfully augmenting agents requires learning how to request expert reasoning, moving beyond simple requests for help, and this work introduces AHCE (Active Human-Augmented Challenge Engagement), a framework for on-demand Human-AI collaboration.

Abstract

Large Language Model (LLM) based agents excel at general reasoning but often fail in specialized domains where success hinges on long-tail knowledge absent from their training data. While human experts can provide this missing knowledge, their guidance is often unstructured and unreliable, making its direct integration into an agent's plan problematic. To address this, we introduce AHCE (Active Human-Augmented Challenge Engagement), a framework for on-demand Human-AI collaboration. At its core, the Human Feedback Module (HFM) employs a learned policy to treat the human expert as an interactive reasoning tool. Extensive experiments in Minecraft demonstrate the framework's effectiveness, increasing task success rates by 32% on normal difficulty tasks and nearly 70% on highly difficult tasks, all with minimal human intervention. Our work demonstrates that successfully augmenting agents requires learning how to request expert reasoning, moving beyond simple requests for help.

Requesting Expert Reasoning: Augmenting LLM Agents with Learned Collaborative Intervention

TL;DR

It is demonstrated that successfully augmenting agents requires learning how to request expert reasoning, moving beyond simple requests for help, and this work introduces AHCE (Active Human-Augmented Challenge Engagement), a framework for on-demand Human-AI collaboration.

Abstract

Large Language Model (LLM) based agents excel at general reasoning but often fail in specialized domains where success hinges on long-tail knowledge absent from their training data. While human experts can provide this missing knowledge, their guidance is often unstructured and unreliable, making its direct integration into an agent's plan problematic. To address this, we introduce AHCE (Active Human-Augmented Challenge Engagement), a framework for on-demand Human-AI collaboration. At its core, the Human Feedback Module (HFM) employs a learned policy to treat the human expert as an interactive reasoning tool. Extensive experiments in Minecraft demonstrate the framework's effectiveness, increasing task success rates by 32% on normal difficulty tasks and nearly 70% on highly difficult tasks, all with minimal human intervention. Our work demonstrates that successfully augmenting agents requires learning how to request expert reasoning, moving beyond simple requests for help.
Paper Structure (21 sections, 2 equations, 4 figures, 2 tables)

This paper contains 21 sections, 2 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Reinforcement Learning with LLMs
  4. Agents in Minecraft
  5. Preliminary Study: Limitations of a Zero-Shot Minecraft Agent
  6. Baseline Implementation
  7. Preliminary Experiments
  8. AHCE Framework
  9. Overview
  10. Problem Identification Module
  11. Human Feedback Module
  12. Interactive Reasoning with a Human-in-the-Loop.
  13. Query Execution Module
  14. Guiding the Planner.
  15. Guiding the Performer.
  16. ...and 6 more sections

Figures (4)

  • Figure 1: Illustration of the multi-layered knowledge gap in domain-specific tasks. An agent's failure can stem from a missing factual rule (top path, trying to mine stone without a pickaxe) or a missing strategic heuristic (bottom path, not knowing to dig down for stone). Our approach (bottom path) leverages on-demand human expertise to address both types of failures, enabling successful task completion.
  • Figure 2: An overview of our AHCE framework, which prioritizes autonomous self-correction (the red loop). When a critical impasse is detected, the system seamlessly transitions to solicit targeted human feedback (the green loop), enabling the agent to overcome knowledge gaps and achieve the task.
  • Figure 3: The operational logic of the Problem Identification Module (Top) and Query Execution Modules (Bottom).
  • Figure 4: The overview of HFM. (a) The GRPO pipeline. (b) The detail of the rollout generation process.