Enhancing LLM Planning Capabilities through Intrinsic Self-Critique

February 09, 2026

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📝 Original Info

Title: Enhancing LLM Planning Capabilities through Intrinsic Self-Critique
ArXiv ID: 2512.24103
Date: 2025-12-30
Authors: Bernd Bohnet, Pierre-Alexandre Kamienny, Hanie Sedghi, Dilan Gorur, Pranjal Awasthi, Aaron Parisi, Kevin Swersky, Rosanne Liu, Azade Nova, Noah Fiedel

📝 Abstract

We demonstrate an approach for LLMs to critique their own answers with the goal of enhancing their performance that leads to significant improvements over established planning benchmarks. Despite the findings of earlier research that has cast doubt on the effectiveness of LLMs leveraging self critique methods, we show significant performance gains on planning datasets in the Blocksworld domain through intrinsic self-critique, without external source such as a verifier. We also demonstrate similar improvements on Logistics and Mini-grid datasets, exceeding strong baseline accuracies. We employ a few-shot learning technique and progressively extend it to a many-shot approach as our base method and demonstrate that it is possible to gain substantial improvement on top of this already competitive approach by employing an iterative process for correction and refinement. We illustrate how self-critique can significantly boost planning performance. Our empirical results present new state-of-the-art on the class of models considered, namely LLM model checkpoints from October 2024. Our primary focus lies on the method itself, demonstrating intri...

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Enhancing LLM Planning Capabilities through Intrinsic Self-Critique

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