# LLM reasoning and agentic safety at ICML 2026

DevFeed: [LLM reasoning and agentic safety at ICML 2026](<https://devfeed.tech/articles/llm-reasoning-and-agentic-safety-at-icml-2026-22575.md>)

Original publisher: [Read original article](<https://medium.com/capital-one-tech/llm-reasoning-and-agentic-safety-at-icml-2026-55f341e21caa?source=rss----3db3a67cb648---4>)

Author: Capital One Tech

Published: 2026-07-02T14:14:40Z

Content type: article

Language: en

Sources: [Capital One Tech](<https://devfeed.tech/sources/capital-one-tech.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Trustworthy AI](<https://devfeed.tech/topics/trustworthy-ai.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-research](<https://devfeed.tech/tags/ai-research.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [icml](<https://devfeed.tech/tags/icml.md>), [icml-2026](<https://devfeed.tech/tags/icml-2026.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llm-reasoning](<https://devfeed.tech/tags/llm-reasoning.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [research](<https://devfeed.tech/tags/research.md>), [safety](<https://devfeed.tech/tags/safety.md>), [science](<https://devfeed.tech/tags/science.md>), [trustworthy-ai](<https://devfeed.tech/tags/trustworthy-ai.md>)

## AI overview

Capital One presents research for ICML 2026 on critique-guided distillation for robust LLM reasoning and on safety risks in multi-turn tool-using agents. The article says its Critique-Guided Distillation framework trains models to refine flawed responses using teacher critiques, and reports higher mathematical-reasoning benchmark performance than critique fine-tuning and standard distillation, including a 7% average improvement and gains of up to 15.0% on AMC23 and 12.2% on MATH-500.

## Source excerpt

Explore our latest research in critique-guided distillation and multi-turn agent uncertainty in Seoul.Explore our latest research in critique-guided distillation and multi-turn agent uncertainty in Seoul. Capital One technologists are excited to participate in the 43rd International Conference on Machine Learning (ICML) taking place at the COEX Convention & Exhibition Center in Seoul, South Korea, July 6-11, 2026. As a premier global venue for machine learning research, ICML provides an essential forum for exploring foundational advancements, algorithmic innovations and cutting-edge deep learning systems. Capital One is excited to share advancements in large language model (LLM) scaling efficiencies, multi-turn tool-using agent safety and the development of robust, trustworthy AI frameworks. This work delivers the underlying engineering and algorithmic improvements crucial for deploying the next generation of safe financial technologies. Main conference research: Robust reasoning and agentic risk The following research, accepted to the ICML Main Conference, pushes the boundaries of how models self-correct, how trajectory-level risks can be proactively flagged, and how multi-turn agent interactions maintain reliable execution. This section features work led by Capital One researchers alongside deep collaborations with academic partners. Critique-Guided Distillation for Robust Reasoning via Refinement Capital One Authors: Berkcan Kapusuzoglu, Supriyo Chakraborty, Michael Lee, Sambit Sahu Supervised fine-tuning with expert demonstrations often produces models that imitate outputs without internalizing the reasoning processes needed for robust generalization. While critique-based approaches show promise, training models to generate critiques directly, such as Critique Fine-Tuning (CFT), can lead to output-format drift and degradation of general capabilities. We propose Critique-Guided Distillation (CGD), a training framework that decouples critique consumption from crit