Training Advisors for LLM Agents from Task Outcomes

2026-10-07

AI Summary

This study addresses the lack of effective natural language feedback for large language model (LLM) agents in multi-step tasks by proposing Caddie. This method trains a critic model via reinforcement learning, optimizing end-to-end using only task-level success or failure signals without requiring step-level annotations or reference critiques, thereby providing agents with real-time decision-making guidance. Experiments employing a 4B-parameter critic demonstrate that Caddie achieves zero-shot transferability across models and domains. On the MuSiQue benchmark, it improves the success rates of multiple base models by over 25 percentage points and surpasses the performance of Kimi K3 operating without a critic.

Abstract

Large language model agents tackle multi-step tasks by interleaving reasoning and tool calls with observations from the environment. Prior work has shown that natural-language feedback can help these agents revise their decisions during task execution. We introduce Caddie, a method for training critics to provide natural-language analysis and advice as agents work through a task. Unlike approaches that rely on step-level labels or reference critiques, Caddie learns from whether the agent ultimately succeeds after receiving the critic's feedback. We optimize the critic through reinforcement learning while keeping the base model frozen. Trained on multi-hop question answering with a single base model, our Qwen3-4B critic improves success rates across four base models of different scales and architectures, including three not used during critic training. On the MuSiQue benchmark, the trained critic improves Qwen3-4B's success rate by more than 25 percentage points, surpassing the performance of Kimi K3 without a critic. The same critic also yields gains on out-of-domain interactive benchmarks, including $τ^3$ and DeepDive, with no additional training. Our results show that agents can decide when to seek help from a critic at inference time and that outcome-based critic training can produce guidance that transfers across base models and task domains.

Problem

• LLM agents
• natural-language feedback
• critic training
• outcome-based learning
• cross-model transfer

Innovation

• Reinforcement Learning
• LLM Agents
• Outcome-based Training
• Cross-model Transfer
• Natural Language Critic