#NeurIPS2025 reflections After a week of posters, hallway chats, and workshop deep dives, here are a few themes that stood out... Training strategies and RL's evolving role: @YejinChoinka's keynote pushed past the "scale-is-all" narrative with a focus on RL--not just as a fine-tuning method, but as part of the pre-training story. The idea of eliciting reasoning behaviors in smaller models through RL is gaining traction. Benchmark design and meta-evaluation: From Terminal-Bench to agent self-evaluation setups, we're seeing a shift toward evaluating not just outputs but internal reasoning and feedback loops. There's an emerging science of how we measure capability, not just completion. LLMs in software environments: Papers like SWE-smith and SWE-rebench explored how LLMs perform in interactive dev environments. Robustness to tool changes and realistic regression testing stood out--especially relevant for agent-based use cases. From scale to specificity: A subtle but important theme...quality, provenance, and realism in data pipelines (both human-labeled and synthetic) are being treated as first-class problems. That feels like a healthy turn from quantity-first mindsets. Takeaway: NeurIPS isn't just about bigger models anymore--it's about sharper questions. #NeurIPS #LLMs #AIresearch