Evidence brief
The findings can help multilingual reasoning models choose reinforcement learning training languages.
Research from Apple Machine Learning Research examines GRPO performance in multilingual and non-English environments, covering multiple base models, training languages, and inference-language reward settings.
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Source attributionApple Machine Learning Research（RSS） · Apple Machine Learning Research（RSS）
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Université Paris-Saclay and other institutions proposed MVICAD2, allowing brain sources from different subjects to vary in time delay and dilation; simulations show it outperforms existing methods, and the Cam-CAN dataset verifies the correlation.
Why it mattersThis method provides finer-grained multi-subject temporal difference modeling for analyzing brain dynamics such as auditory stimuli.
Original sourceThe posting account says the latest episode of Max Agency explains how @unifygtm cut model costs by 90–95% in the two weeks before launch, and lists YouTube, Apple, and Spotify listening links.
Why it mattersThis case provides useful project retrospective clues for controlling model costs before release.
Original source@omarsar0 reposted that this is a very interesting paper and recommended it to anyone interested in training agents with existing harnesses; the original post did not provide the paper title or conclusions.
Why it mattersThis information can help people tracking Agent training find leads on related papers.
Original sourceThe repost says Large Discovery Models study “learning where to search next,” using methods such as LLM-generated candidates followed by uncertainty evaluation with a Bayesian surrogate.
Why it mattersThis method combines LLM generation with uncertainty scoring to guide search directions.
Original source@HuggingPapers reposted that HarnessEval-W is a new benchmark that brings the harness paradigm into evaluation in visual worlds.
Why it mattersThis benchmark provides a new task structure and comparison method for evaluating visual world models.
Original source