An arXiv preprint from a team at Tsinghua University, AgiBot and Infinigence AI introduces collective experience learning (CEL), a paradigm in which deployed robots share execution experience to improve one common policy. Its algorithm, failure-guided experience retrieval fine-tuning (FERT), uses the context of a failed execution to retrieve relevant successful trajectory segments from the fleet's shared experience and trains on their actions, requiring no extra expert demonstrations or corrective actions — humans only label success or failure and reset the environment. A fault-tolerant asynchronous training system (FATS) keeps collection, optimization and distribution running through individual robot interruptions. The authors report 93.3–99.2% success across five real-world manipulation tasks over 120 trials each on robots excluded from training data collection, and a rise from 79.2% to 98.3% on multi-stage phone packaging when learning with 96 robots, while noting evaluation was limited to five task-specific policies on a single platform.