Learning from Trials and Errors: Reflective Test-Time Planning for Embodied LLMs (Feb 2026)

Описание: Title: Learning from Trials and Errors: Reflective Test-Time Planning for Embodied LLMs (Feb 2026)
Link: http://arxiv.org/abs/2602.21198v1
Date: February 2026

Summary:
This paper introduces Reflective Test-Time Planning, a framework that enables embodied LLMs to learn from mistakes during deployment. It integrates three reflection modes: reflection-in-action for pre-execution simulation, reflection-on-action for updating policies based on outcomes, and retrospective reflection for hindsight credit assignment. The approach allows robots to accumulate experience and recover from failures in complex, long-horizon tasks.

Key Topics:
Embodied LLMs
Reflective Test-Time Planning
Reflection-in-action
Reflection-on-action
Retrospective Reflection
Test-Time Training (TTT)
Robotic Task Planning
Failure Recovery

Chapters:
00:00 - Introduction & Research Overview
01:30 - Solving Static Oracle Problems
02:45 - Double Loop Learning Theory
04:15 - Reflection In Action Mechanics
06:05 - Sampling Optimal Candidate Actions
08:06 - Tuning Sampling Temperature
09:23 - Reflection On Action Logic
10:48 - Implementing Retrospective Hindsight
12:15 - Real-time Parameter Updates
13:15 - Optimizing LoRA Hyperparameters
14:10 - Analyzing Benchmark Results
16:05 - Comparing Compute Efficiency
17:15 - Planning Horizon Limitations
18:25 - Zero-shot Generalization Testing
20:05 - Future Of Autonomous Learning

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