TLDR Tech / AI
Experiential Reinforcement Learning
ERL trains policies with an explicit attempt → feedback → reflection → revised attempt loop, then reinforced the successful revision back into the base model. The approach improves sparse-reward learning and tool-using reasoning performance while keeping deployment-time inference cost unchanged.
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