主播
节目简介
来源:小宇宙
你有没有想过,让AI变得更聪明,关键可能不是让它知道得更多,而是教会它如何更高效地“思考”?本期我们要聊的几篇最新论文,就深入到了AI的思维深处:从让AI懂得“选择性遗忘”以实现长时间推理,到揭开决定AI学习成败的三个神秘“开关”。我们甚至会看到,机器人是如何通过“自言自语”来规划复杂任务的。准备好一起探索AI大脑的内部运作机制了吗?我们马上开始!
00:00:33 如何让AI长时间思考,还不“累”?
00:05:05 给你一个确定性的菜谱,靠谱吗?
00:10:44 你关心的问题,AI能比专家更快找到答案吗?
00:16:24 拆开AI的“黑箱”,决定它聪明的三个开关
00:22:50 机器人会思考,需要分几步?
本期介绍的几篇论文:
[CL] Prefix Sliding for efficient test-time scaling
[Stanford University & University of California at Santa Barbara & University of Washington]
https://arxiv.org/abs/2608.26070
---
[LG] Beyond Optimal Rates in Stochastic Optimization: Trajectory-Adaptive Stopping Rules
[UC Berkeley & PSL Research University]
https://arxiv.org/abs/2608.25551
---
[AI] Planetary Prediction Engine: Autonomous Geospatial Prediction via Intelligent Data Selection and Foundation Model Embeddings
[Google Research]
https://arxiv.org/abs/2608.26088
---
[LG] Demystifying Reinforcement Learning Post-Training of Language Models
[University of Washington]
https://arxiv.org/abs/2608.24949
---
[RO] R^3: Training Robots to Reason in Natural Language via Reinforcement Learning
[Carnegie Mellon University (CMU)]
https://arxiv.org/abs/2608.26053
00:00:33 如何让AI长时间思考,还不“累”?
00:05:05 给你一个确定性的菜谱,靠谱吗?
00:10:44 你关心的问题,AI能比专家更快找到答案吗?
00:16:24 拆开AI的“黑箱”,决定它聪明的三个开关
00:22:50 机器人会思考,需要分几步?
本期介绍的几篇论文:
[CL] Prefix Sliding for efficient test-time scaling
[Stanford University & University of California at Santa Barbara & University of Washington]
https://arxiv.org/abs/2608.26070
---
[LG] Beyond Optimal Rates in Stochastic Optimization: Trajectory-Adaptive Stopping Rules
[UC Berkeley & PSL Research University]
https://arxiv.org/abs/2608.25551
---
[AI] Planetary Prediction Engine: Autonomous Geospatial Prediction via Intelligent Data Selection and Foundation Model Embeddings
[Google Research]
https://arxiv.org/abs/2608.26088
---
[LG] Demystifying Reinforcement Learning Post-Training of Language Models
[University of Washington]
https://arxiv.org/abs/2608.24949
---
[RO] R^3: Training Robots to Reason in Natural Language via Reinforcement Learning
[Carnegie Mellon University (CMU)]
https://arxiv.org/abs/2608.26053