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Discarding just a few high-advantage rollouts during GRPO updates dramatically boosts LLM reasoning and exploration.
VLMs exhibit striking variability in their ability to correct repeated false premises, with some models showing persistent inaccuracies that could mislead users.
Even the most advanced LLMs struggle to maintain narrative consistency, with a staggering 68% of generated content conflicting under user interventions.
CRISP reduces interaction turns by up to 33% while maintaining accuracy by prioritizing critical evidence-gathering steps in deep search agents.
Reducing reasoning tokens while boosting accuracy, SuCo transforms how LRMs approach problem-solving by focusing on sufficiency rather than excess.
LLMs can translate long documents far more effectively by learning to selectively attend to relevant context, mimicking human translation strategies.
Multi-agent systems get a 6.3% accuracy boost on math problems thanks to a new "rectify-or-reject" pruning method that dynamically filters out bad information at test time.