6 citations · 6 across the 14 of their papers we have counts for
7 papers · 1 filter
Rationale Distribution Distillation via Flow Matching for Distributional Continuous Latent Reasoning
Mingyeong Kim, Gyusang Cho, Juho Lee +1
Sampling multiple chain-of-thought rationales from a language model can uncover correct solutions missed by a single attempt. However, these rationales are typically lengthy, makin…
Better Slots, Better Worlds: Representation Quality & Robustness in Object-Centric World Models
Shukrullo Nazirjonov, Sai Prasanna, Anna Manasyan +1
Learning world models from offline trajectories enables agents to accomplish different tasks through planning. Object-centric (OC) representations, which decompose a scene into a s…
Shift & Drift: A Zero-Shot Benchmark for Generalizable and Robust Autonomous Driving Motion Planning
Alessandro Canevaro, Hang Yu, Julian Schmidt +5
While closed-loop motion planners trained on large-scale, object-level datasets, e.g., nuPlan, demonstrate strong in-distribution (ID) performance, their generalization to novel ur…
Scaling Laws and Tradeoffs in Recurrent Networks of Expressive Neurons
Aaron Spieler, Georg Martius, Anna Levina
Cortical neurons are complex, multi-timescale processors wired into recurrent circuits, shaped by long evolutionary pressure under stringent biological constraints. Mainstream mach…
Drifting Fields are not Conservative
Leonard T. Franz, Sebastian Hoffmann, Tim Weiland +2
Drifting models have recently gained attention for generating high-quality samples in a single forward pass. During training, they learn a push-forward map by following a vector-va…
GASP: Guided Asymmetric Self-Play For Coding LLMs
Swadesh Jana, Cansu Sancaktar, Tomáš Daniš +3
Asymmetric self-play has emerged as a promising paradigm for post-training large language models, where a teacher continually generates questions for a student to solve at the edge…
Soft Forward-Backward Representations for Zero-shot Reinforcement Learning with General Utilities
Marco Bagatella, Thomas Rupf, Georg Martius +1
Recent advancements in zero-shot reinforcement learning (RL) have facilitated the extraction of diverse behaviors from unlabeled, offline data sources. In particular, forward-backw…