Presenting A Sober Look at Progress in LM Reasoning at @colmweb.org today 🇨🇦 #COLM2025 📅 Today 🕔 11:00 AM – 1:00 PM 📍 Room 710 - Poster #31 We find that many “reasoning” gains fall within variance and show how to make evaluation reproducible again. 📘 bethgelab.github.io/sober-reasoning

Excited about this new work from @haoyuhe.bsky.social. TLDR: Diffusion language models treat learning and inference differently which lowers performance. RL can be used to overcome this issue for certain problems.

🧵1/ 🚨 New paper: A Sober Look at Progress in Language Model Reasoning We re-evaluate recent SFT and RL models for mathematical reasoning and find most gains vanish under rigorous, multi-seed, standardized evaluation. 📊 bethgelab.github.io/sober-reason... 📄 arxiv.org/abs/2504.07086

New preprint out! 🎉 How does LLM training loss translate to downstream performance? We show that pretraining data and tokenizer shape loss-to-loss scaling, while architecture and other factors play a surprisingly minor role! brendel-group.github.io/llm-line/ 🧵1/8

CuratedThoughts: Data Curation for RL Datasets 🚀 Since DeepSeek-R1 introduced reasoning-based RL, datasets like Open-R1 & OpenThoughts emerged for fine-tuning & GRPO. Our deep dive found major flaws — 25% of OpenThoughts needed elimination by data curation. Here's why 👇🧵

SWE-bench Multimodal evaluation code is out now! SWE-bench MM is a new set of JavaScript issues that have a visual component (‘map isn’t rendering correctly’, ‘button text isn’t appearing’). www.swebench.com/sb-cli/

We are presenting CiteMe today at the 11AM poster session (East Exhibit Hall A-C, #3309) CiteMe is a challenging benchmark for LM-based agents to find paper citations, moving beyond simple multiple-choice Q&A to real-world use cases. Come by and say hi :) citeme.ai

Here's a fledgling starter pack for the AI community in Tübingen. Let me know if you'd like to be added! go.bsky.app/NFbVzrA