Will you be at #NeurIPS2025? Come talk TMLR and collect swag!
EiCs Gautam Kamath (@gautamkamath.com) and Nihar Shah will be there -- if you are an AE or an Expert Reviewer, or have a Featured or Outstanding Certification, you can get a free TMLR laptop sticker! Locations ⬇️
Our discussion period just started. Authors, please read our instructions carefully. We require responses by June 2.
But, what you really want to hear about is stats .... right? -> 🧵
o3’s weird hallucinations could indicate they used llm as a judge (or other softer verifiers) in high volume and in addition to math/code correctness.
This addition lets OpenAI scale RL by making more data available to train on, but has new downstream problems to solve.
One of the first papers I've seen with RLVR / reinforcement finetuning of vision language models
Looks about as simple as we would expect it to be, lots of details to uncover.
Liu et al. Visual-RFT: Visual Reinforcement Fine-Tuning
buff.ly/DbGuYve
(posted a week ago, oops)
Monitoring Reasoning Models for Misbehavior and the Risks of
Promoting Obfuscation cdn.openai.com/pdf/34f2ada6...
Open-Reasoner-Zero: An Open Source Approach to Scaling Up
Reinforcement Learning on the Base Model
github.com/Open-Reasone...
MoBA: Mixture of Block Attention for Long-Context LLMs github.com/MoonshotAI/M...
Preference Modeling: Binary Discrimination Versus Imitation Learning: swtheking.notion.site/182d3429a807...
Rare that a paper these days uses the original literature of "Outcome reward model" and not just doing bradley-terry model on right/wrong labels.
Nature is healing.
Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning
Lyu et al
arxiv.org/abs/2502.06781
Examining False Positives under Inference Scaling for Mathematical Reasoning arxiv.org/pdf/2502.06217
oatllm.notion.site/oat-zero
There May Not be Aha Moment in R1-Zero-like Training — A Pilot Study
#Papers
This is a potentially counterintuitive result. We actually want the reasoning models to generate more tokens for wrong answers. Eventually, models should “know” when they’re not right and keep spending more compute on it!
Regardless, is a great plot.
arxiv.org/abs/2501.18585
✍️ Reminder to reviewers: Check author responses to your reviews, and ask follow up questions if needed.
50% of papers have discussion - let’s bring this number up!
