StepCodeReasoner aligns code reasoning with verifiable stepwise execution traces via print anchors and bi-level GRPO reinforcement learning, reaching SOTA results on CRUXEval (91.1%) and LiveCodeBench (86.5%) for a 7B model.
On the diversity of synthetic data and its impact on training large language models
6 Pith papers cite this work. Polarity classification is still indexing.
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Gemma 3 27B and Aya Expanse 32B are the strongest multilingual synthetic-data teachers; model scale does not predict effectiveness while prompt diversity, length and response fluency do.
Fine-tuning LLMs on multi-source synthetic data mitigates distribution collapse and self-preference bias while increasing output quality relative to single-source or human-only fine-tuning.
Proposes a five-bucket taxonomy of LLM harms and calls for dynamic auditing, but the systematic review behind it is not reproducible and contains mismatched citations.
A literature survey that organizes prompting, fine-tuning, preference optimization, and context-aware techniques for LLM-based machine translation with emphasis on low-resource languages.
Position paper claiming that distributed training across massive edge devices can overcome data depletion and centralized compute monopolies in LLM scaling.
citing papers explorer
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StepCodeReasoner: Aligning Code Reasoning with Stepwise Execution Traces via Reinforcement Learning
StepCodeReasoner aligns code reasoning with verifiable stepwise execution traces via print anchors and bi-level GRPO reinforcement learning, reaching SOTA results on CRUXEval (91.1%) and LiveCodeBench (86.5%) for a 7B model.
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Polyglot Teachers: Evaluating Language Models for Multilingual Synthetic Data Generation
Gemma 3 27B and Aya Expanse 32B are the strongest multilingual synthetic-data teachers; model scale does not predict effectiveness while prompt diversity, length and response fluency do.
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Synthetic Eggs in Many Baskets: The Impact of Synthetic Data Diversity on LLM Fine-Tuning
Fine-tuning LLMs on multi-source synthetic data mitigates distribution collapse and self-preference bias while increasing output quality relative to single-source or human-only fine-tuning.
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LLM Harms: A Taxonomy and Discussion
Proposes a five-bucket taxonomy of LLM harms and calls for dynamic auditing, but the systematic review behind it is not reproducible and contains mismatched citations.
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Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation
A literature survey that organizes prompting, fine-tuning, preference optimization, and context-aware techniques for LLM-based machine translation with emphasis on low-resource languages.
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Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices
Position paper claiming that distributed training across massive edge devices can overcome data depletion and centralized compute monopolies in LLM scaling.