Month in 4 Papers (March 2025)

Author(s): Ala Falaki, PhD

Originally published on Towards AI.

Efficient reasoning, context extraction, and model scaling innovations in today’s cutting-edge NLP research.

This series of posts is designed to bring you the newest findings and developments in the NLP field. I’ll delve into four significant research papers each month, offering a comprehensive summary. Be sure to visit my blog regularly or subscribe to my newsletter for monthly updates. Let’s dive in!

📝 s1: Simple test-time scaling [paper]

Test-time scaling is a fairly effective method that forces LLMs to think (provide reasoning steps) before providing the final answer. This research dives into data curation and controlling the thinking process. The first takeaway of this paper is curating the 1K dataset with questions and reasoning traces filtered by:

1) difficulty: Where two models (Qwen 7/32) generate answers, with Claude Sonet acting as a judge, and the sample is selected only if both models get the answer wrong.

2) Diversity/Quality: Remove samples with a formatting issue and uniformly select samples from multiple clusters. (Math, AGIEval, OlympicArena,…) The preference is for samples with longer reasoning traces that translate to more difficult questions.

The next step is to control the reasoning budget by appending “Final Answer:” when the maximum thinking tokens are used to force the model to answer the… Read the full blog for free on Medium.

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Published via Towards AI