Learning from Trials and Errors: Reflective Test-Time Planning for Embodied LLMs
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918520612192256 |
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| author | Hong, Yining Huang, Huang Li, Manling Fei-Fei, Li Guibas, Leonidas Wu, Jiajun Choi, Yejin |
| author_facet | Hong, Yining Huang, Huang Li, Manling Fei-Fei, Li Guibas, Leonidas Wu, Jiajun Choi, Yejin |
| contents | Embodied LLMs endow robots with high-level task reasoning, but they cannot reflect on what went wrong or why, turning deployment into a sequence of independent trials where mistakes repeat rather than accumulate into experience. Drawing upon human reflective practitioners, we introduce Reflective Test-Time Planning, which integrates two modes of reflection: \textit{reflection-in-action}, where the agent uses test-time scaling to generate and score multiple candidate actions using internal reflections before execution; and \textit{reflection-on-action}, which uses test-time training to update both its internal reflection model and its action policy based on external reflections after execution. We also include retrospective reflection, allowing the agent to re-evaluate earlier decisions and perform model updates with hindsight for proper long-horizon credit assignment. Experiments on our newly-designed Long-Horizon Household benchmark and MuJoCo Cupboard Fitting benchmark show significant gains over baseline models, with zero-shot generalization to photorealistic HM3D environments and real-robot experiments on a Franka Panda arm. Ablations confirm that reflection-in-action and reflection-on-action are mutually dependent, and that retrospective reflection achieves better credit assignment than step-wise external feedback at lower computational overhead. Qualitative analyses further highlight behavioral correction through reflection. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_21198 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning from Trials and Errors: Reflective Test-Time Planning for Embodied LLMs Hong, Yining Huang, Huang Li, Manling Fei-Fei, Li Guibas, Leonidas Wu, Jiajun Choi, Yejin Machine Learning Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Robotics Embodied LLMs endow robots with high-level task reasoning, but they cannot reflect on what went wrong or why, turning deployment into a sequence of independent trials where mistakes repeat rather than accumulate into experience. Drawing upon human reflective practitioners, we introduce Reflective Test-Time Planning, which integrates two modes of reflection: \textit{reflection-in-action}, where the agent uses test-time scaling to generate and score multiple candidate actions using internal reflections before execution; and \textit{reflection-on-action}, which uses test-time training to update both its internal reflection model and its action policy based on external reflections after execution. We also include retrospective reflection, allowing the agent to re-evaluate earlier decisions and perform model updates with hindsight for proper long-horizon credit assignment. Experiments on our newly-designed Long-Horizon Household benchmark and MuJoCo Cupboard Fitting benchmark show significant gains over baseline models, with zero-shot generalization to photorealistic HM3D environments and real-robot experiments on a Franka Panda arm. Ablations confirm that reflection-in-action and reflection-on-action are mutually dependent, and that retrospective reflection achieves better credit assignment than step-wise external feedback at lower computational overhead. Qualitative analyses further highlight behavioral correction through reflection. |
| title | Learning from Trials and Errors: Reflective Test-Time Planning for Embodied LLMs |
| topic | Machine Learning Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2602.21198 |