Learning from Trials and Errors: Reflective Test-Time Planning for Embodied LLMs
#Embodied LLMs #Reflective Planning #Test-Time Planning #Robot Learning #Error Correction #Long-Horizon Tasks #Cognitive Architecture
📌 Key Takeaways
- Researchers developed Reflective Test-Time Planning for embodied LLMs to enable learning from mistakes
- The approach integrates two reflection modes: reflection-in-action and reflection-on-action
- Experiments showed significant gains over baseline models on household and cupboard fitting tasks
- Real-robot trials demonstrated tangible behavioral correction through reflection
📖 Full Retelling
A team of researchers including Yining Hong, Huang Huang, Manling Li, Li Fei-Fei, Jiajun Wu, and Yejin Choi introduced Reflective Test-Time Planning for embodied LLMs in a paper submitted to arXiv on February 24, 2026, addressing the limitation that current robotic systems cannot reflect on their mistakes, turning deployment into a sequence of independent trials where errors repeat rather than accumulate into experience. The researchers drew inspiration from human reflective practitioners to develop a novel approach that enables robots to learn from their experiences rather than repeatedly making the same errors. Their methodology integrates two distinct modes of reflection: reflection-in-action, where the agent uses test-time scaling to generate and score multiple candidate actions using internal reflections before execution; and reflection-on-action, which uses test-time training to update both internal reflection models and action policies based on external reflections after execution. Additionally, the system includes retrospective reflection capabilities, allowing agents to re-evaluate earlier decisions and perform model updates with hindsight for proper long-horizon credit assignment. The researchers validated their approach through experiments on their newly-designed Long-Horizon Household benchmark and MuJoCo Cupboard Fitting benchmark, demonstrating significant performance gains over baseline models. Ablative studies confirmed the complementary roles of reflection-in-action and reflection-on-action, while qualitative analyses including real-robot trials highlighted tangible behavioral correction through the reflection process.
🏷️ Themes
Machine Learning, Robotics, Artificial Intelligence
📚 Related People & Topics
Error detection and correction
Reliable digital data delivery methods on unreliable channels
In information theory and coding theory with applications in computer science and telecommunications, error detection and correction (EDAC) or error control are techniques that enable reliable delivery of digital data over unreliable communication channels. Many communication channels are subject to...
Entity Intersection Graph
Connections for Error detection and correction:
View full profileOriginal Source
--> Computer Science > Machine Learning arXiv:2602.21198 [Submitted on 24 Feb 2026] Title: Learning from Trials and Errors: Reflective Test-Time Planning for Embodied LLMs Authors: Yining Hong , Huang Huang , Manling Li , Li Fei-Fei , Jiajun Wu , Yejin Choi View a PDF of the paper titled Learning from Trials and Errors: Reflective Test-Time Planning for Embodied LLMs, by Yining Hong and 5 other authors View PDF HTML Abstract: 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 ablative studies validating the complementary roles of reflection-in-action and reflection-on-action. Qualitative analyses, including real-robot trials, highlight behavioral correction through reflection. Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV cs.RO) Cite as: arXiv:2602.21198 [cs.LG] (or arXiv:2602.21198v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.21198 Focus to learn more arXiv-issued DOI via DataC...
Read full article at source