ORPO performs preference alignment during supervised fine-tuning via a monolithic odds ratio penalty, allowing 7B models to outperform larger state-of-the-art models on alignment benchmarks.
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ST-MoE introduces stability techniques for sparse expert models, allowing a 269B-parameter model to achieve state-of-the-art transfer learning results across reasoning, summarization, and QA tasks at the compute cost of a 32B dense model.
CalibAdv calibrates GRPO advantage signals for search agents by downscaling excessive negative advantages using intermediate-step correctness, improving performance and training stability across three models and seven benchmarks.
PaSaMaster, a recursive self-evolving agentic literature retrieval system, reports 16.5× higher F1 than Google Scholar and 37.8% higher F1 than GPT-5.2 on PaSaMaster-Bench across 38 disciplines with zero source hallucination at ~1% cost.
Proposes Modality-Aware Credit Assignment (MoCA) with blindfolded-reasoning proxy to reward perception fidelity separately from reasoning in VLMs.
citing papers explorer
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ST-MoE: Designing Stable and Transferable Sparse Expert Models
ST-MoE introduces stability techniques for sparse expert models, allowing a 269B-parameter model to achieve state-of-the-art transfer learning results across reasoning, summarization, and QA tasks at the compute cost of a 32B dense model.
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Negative Advantages Is a Double-Edged Sword: Calibrating advantages in GRPO for Search Agents
CalibAdv calibrates GRPO advantage signals for search agents by downscaling excessive negative advantages using intermediate-step correctness, improving performance and training stability across three models and seven benchmarks.
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Towards Recursive Self-Evolving Agentic Literature Retrieval
PaSaMaster, a recursive self-evolving agentic literature retrieval system, reports 16.5× higher F1 than Google Scholar and 37.8% higher F1 than GPT-5.2 on PaSaMaster-Bench across 38 disciplines with zero source hallucination at ~1% cost.
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Bad Seeing or Bad Thinking? Rewarding Perception for Multimodal Reasoning
Proposes Modality-Aware Credit Assignment (MoCA) with blindfolded-reasoning proxy to reward perception fidelity separately from reasoning in VLMs.