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Achieve the best of both worlds in LLM policy optimization: SRPO combines the rapid gains of self-distillation with the long-term stability of group-relative methods, outperforming both by adaptively routing samples.
Factually dubious LLM outputs can be tamed by strategically penalizing high-confidence predictions at "risky" tokens during fine-tuning, guided by sentence-level factuality labels.
On-Policy Distillation could be the key to more robust and reliable LLM knowledge transfer, but the field is fragmented and lacks a unified theoretical understanding.
By tightly coupling reasoning, searching, and generation, Unify-Agent demonstrates that agent-based modeling can substantially improve world knowledge grounding in image synthesis, rivaling closed-source models.
LLM-as-a-judge consensus is often an illusion: models agree on surface-level features, but diverge wildly when evaluating true quality, a problem fixable by injecting domain knowledge into rubrics.
