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arXiv AI

Don't Solve, Just Compare: Tiny Advisors for Runtime Intervention in LLM Agents

The paper introduces COTA, a lightweight comparison-only framework that enables runtime intervention in large language model agents by using a tiny comparator to evaluate whether alternative continuations outperform the agent’s current proposal. Instead of generating full solutions, the comparator only judges sampled alternatives, and repeated comparisons decide when to intervene. Experiments on WebShop, ALFWorld, and tau^3-Retail show that COTA improves performance across nine settings and outperforms existing baselines, demonstrating that constructive intervention can be effective even with a much weaker auxiliary model.

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arXiv Computation and Language
Sep 1

CAST: Critique-Aware Supervision for Training Reliable Long-Horizon Tool-Calling Agents

CAST is a critique‑aware training framework that transforms sparse task outcomes into action‑level supervision for both critique learning and policy optimization. By analyzing agent trajectories, CAST synthesizes structured rationales that explain action validity under partial observability, enabling the creation of richer training data. Fine‑tuned Qwen3‑family models trained with CAST show significant reliability gains, outperforming GPT‑OSS‑120B by over 10% on Retail tasks and improving Telehealth performance by 9% in an out‑of‑domain setting.

By Amir Saeidi, Zehua Zhang, Rishitosh Singh, Naman Ahuja, Vivek Gupta, Ali Payani, Gaowen Liu, Jayanth Srinivasa, Chitta Baral
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arXiv AI
Oct 12

MENTOR: Learning from Failure with On-Policy Pedagogical Intervention

The paper introduces MENTOR, a multi‑agent framework that provides on‑policy pedagogical intervention to address zero‑reward collapse in reinforcement learning from verifiable rewards. MENTOR diagnoses failures in real time and generates targeted prompts to guide the learning agent, integrating seamlessly with existing policy optimization pipelines such as GRPO. Experiments on math and code generation tasks show that MENTOR significantly reduces collapse, achieving up to 78.3% relative gains on Llama‑3.2‑3B‑Instruct.

By Guanlan Yue, Hang Yu, Xiaoyong Huang, Ruipeng Li, Wei Zhang, Bingchang Liu, Rui Wang
llmsagentsreinforcement-learningbenchmarks
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arXiv Computation and Language
Oct 9

Opera: A Verbal Critic Framework for Long-horizon Coding Agents

Opera is a verbal critic framework designed for long‑horizon coding agents, treating each correction as a persistent note that is followed until the problem is resolved. It uses periodic and event‑driven triggers to diagnose issues, audits feedback against visible evidence, and tracks subsequent actions to distinguish compliance from true resolution. In tests, Opera improves the resolve rate of non‑critic agents by up to 12.4, 15.0, and 8.9 percentage points on three benchmarks and provides on‑policy training data that boosts a model’s performance without requiring a critic at inference time.

By Kai Mei, Zhiyuan Hu, Yutong Dai, Juntao Tan, Yifan Zhang, Dingjie Song, Dimitris N. Metaxas, Silvio Savarese, Ran Xu, Zeyuan Chen
agentsfine-tuningbenchmarks
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arXiv AI
Oct 15

CoEvoke: Evoking Multi-Agent Collaboration for Trajectory Collection

arXiv:2610.14021v1 Announce Type: cross Abstract: Training multi-agent collaboration requires trajectories of checking, feedback, and repair, yet task-solving rollouts may end in immediate approval o...

By Lin Wu, Shu Yao, Yuheng Wang, Jingru Fan, Huatao Li, Yihan Li, Liu Yixuan, Cheng Liu, Jian Chang, Zeyu Zhao, Aohua Zhang, Huiying Zhao, Shuyuan Zhu, Chen Qian
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