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Even strong LLMs struggle to pinpoint the exact moment and cause of failure in risky agent trajectories arising from latent, intrinsic issues, achieving below 35 Strict-F1 on risk-step localization.
LLMs can now learn mathematical reasoning 2x faster and with greater stability, thanks to a new token-level policy optimization method.
