The research published on February 15, 2026 in a joint collaboration between University of Southern California, Microsoft and University of Pennsylvania introduces Experiential Reinforcement Learning (ERL), a novel training framework designed to help language models learn from their own interactions more effectively than standard reinforcement learning. Unlike traditional methods that rely solely on numerical rewards, ERL enables agents to verbally reflect on their failures and successes within each training episode. This process involves a cycle of experience, reflection, and consolidation, where the model uses a cross-episode memory to store effective corrective patterns. To ensure these improvements persist without needing reflection during actual use, the system utilizes selective distillation to internalize successful behaviors directly into the base policy. Experimental results across agentic reasoning tasks like Sokoban and FrozenLake show that ERL significantly boosts learning efficiency and final performance. Ultimately, the framework demonstrates that structured self-critique transforms sparse environment feedback into durable, high-quality behavioral changes. Source: February 2026 Experiential Reinforcement Learning University of Southern California, Microsoft, University of Pennsylvania Taiwei Shi, Sihao Chen, Bowen Jiang, Linxin Song, Longqi Yang, Jieyu Zhao https://arxiv.org/pdf/2602.13949