RL Squeezes, SFT Expands: A Comparative Study of Reasoning LLMs
arXiv:2509.21128v2 Announce Type: replace Abstract: Large language models (LLMs) are typically trained by reinforcement learning (RL) with verifiable rewards (RLVR)…
arXiv:2509.21128v2 Announce Type: replace Abstract: Large language models (LLMs) are typically trained by reinforcement learning (RL) with verifiable rewards (RLVR)…
arXiv:2605.19514v2 Announce Type: replace Abstract: Many works make the eye-catching claim that Transformers are Turing-complete. However, the literature often conflates…
arXiv:2605.28302v1 Announce Type: cross Abstract: Modern large language model (LLM) inference has progressively disaggregated to keep pace with growing model…
arXiv:2605.28597v1 Announce Type: cross Abstract: This position paper argues that the AI/ML community should stop overclaiming and retire the label…
arXiv:2605.27258v2 Announce Type: replace-cross Abstract: Building state-of-the-art text-to-speech (TTS) systems typically demands millions of hours of proprietary data and complex…
arXiv:2605.27997v1 Announce Type: cross Abstract: Large language models frequently generate toxic, hateful, or harmful content, yet existing mitigation methods rely…
arXiv:2605.27999v1 Announce Type: cross Abstract: We address the problem of learning to assign prediction tasks to one agent from a…
arXiv:2605.27365v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) commonly formulate visual grounding and detection as a coordinate-token generation problem, serializing…
Nature Machine Intelligence, Published online: 28 May 2026; doi:10.1038/s42256-026-01248-2 Human–AI interactions reshape the self and our social networks