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A learned "Token-to-Go" progress meter enables multi-turn RL agents to effectively learn from sparse rewards, boosting performance by up to 7.2 points without increasing training costs.
LLM agents can improve their decision-making by learning from their own diagnosed failures, with first-proposal corrections boosting success rates by over 30 percentage points.
