alphaXiv shares RL research addressing LLM problem solving inefficiencies
The paper proposes dynamic compute reallocation to focus on hard prompts, overcoming the Matthew Effect where models ignore difficult tasks.
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In short: alphaXiv raised $7M in seed funding and launched Assistant 2.0, introducing a Pro Plan and enhanced tools for scientific discourse.
The paper proposes dynamic compute reallocation to focus on hard prompts, overcoming the Matthew Effect where models ignore difficult tasks.
Pretrained LLMs counteract errors via layers, while high-dimensional geometry preserves top-token predictions at 4-bit precision.
Examines data efficiency, base vs instruct models, population size on Countdown and GSM8K. ES excels in low-data regimes. GRPO superior with more data and base models.
Trains Qwen3-8B on arXivQA dataset from real queries. Achieves competitive recall against frontier models using multi-hop search.
Thanks for your great work! It significantly improves the efficiency and convenience of reading and managing paper. The AI assistant function is great, but sometimes...
AlphaXiv launched a Claude Skill that pulls structured paper overviews via API instead of parsing raw PDFs.
AlphaXiv, an AI research platform, raised a $7 million seed led by Menlo Ventures and Haystack, co-founder Raj Palleti tells Axios exclusively. Why it matters...
There is an inherent tension in the dissemination of research. On one hand, science thrives on openness and communication. On the other, ensuring high-quality scientific...
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