Self-discovering RL in the Era of Experience: Is Learning History an Asset or a Burden?
Moving beyond static scaling toward recursive self-improvement through experiential learning.
The pursuit of recursive self-improvement (RSI) toward general intelligence is divided between macro-level language model scaling and the interaction-driven principles of "Era of Experience". Yet, any self-improving architecture ultimately rests upon its underlying optimization e...
ArXiv CS.AI
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Shifting the focus from massive static datasets to self-improving reinforcement learning allows agents to refine their own optimization strategies through historical interaction. This approach could be the key to overcoming the efficiency limitations of current scaling laws by prioritizing evolutionary learning over simple predictive imitation.
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