Key takeaways:
SPAR: align RL credit to where decisions happen — optimize stage-wise, not via one noisy end reward. Fact-Aware RL: verify atomic claims with retrieval → make hallucination measurable & optimizable
Rubric Evolution: auto-mine & patch adversarial reward hacks.

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