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arXiv — cs.AIMedHopQA benchmark tests LLM reasoning in biomedical Q&A◆arXiv — cs.CLRouters learn geometry of sparse mixture-of-experts◆arXiv — cs.AIStudy audits how LLMs generate political discourse during crises◆arXiv — cs.AIClassifier Context Rot: Monitor Performance Degrades with Context◆arXiv — cs.AIExecutable Agentic Memory for GUI Agent◆arXiv — cs.CLLongMemEval-V2: Evaluating Long-Term Agent Memory Toward Experienced◆arXiv — cs.AISparse-to-dense rewards improve language model post-training◆arXiv — cs.AIAI-native mobility dataset advances 6G handover and beam management◆arXiv — cs.AIMedHopQA benchmark tests LLM reasoning in biomedical Q&A◆arXiv — cs.CLRouters learn geometry of sparse mixture-of-experts◆arXiv — cs.AIStudy audits how LLMs generate political discourse during crises◆arXiv — cs.AIClassifier Context Rot: Monitor Performance Degrades with Context◆arXiv — cs.AIExecutable Agentic Memory for GUI Agent◆arXiv — cs.CLLongMemEval-V2: Evaluating Long-Term Agent Memory Toward Experienced◆arXiv — cs.AISparse-to-dense rewards improve language model post-training◆arXiv — cs.AIAI-native mobility dataset advances 6G handover and beam management◆
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§ research · cluster

Sparse-to-dense rewards improve language model post-training

yesterday · · primary fetch1 sourcecluster 47b1a65cupdated yesterday ·

This cluster groups 2 articles from 1 source. The originating feed didn’t ship an excerpt — open any link below to read the piece.

read full article on arxiv.org ↗
§ sources2 publications · timeline below
  1. arxiv.orgBeyond GRPO and On-Policy Distillation: An Empirical Sparse-to-Dense Reward Principle for Language-Model Post-Trainingprimary
  2. arxiv.orgOGLS-SD: On-Policy Self-Distillation with Outcome-Guided Logit Steering for LLM Reasoning

§ how this story moved

  1. primary — arXiv — cs.AI publishes the launch post.
  2. arXiv — cs.AI picks up coverage.