38 citations · 63 across the 19 of their papers we have counts for
9 papers · 1 filter
More Test-Time Compute Can Hurt: Overestimation Bias in LLM Beam Search
Gal Dalal, Assaf Hallak, Gal Chechik +1
Wider beam search should improve LLM reasoning, but when should you stop widening? Prior work on beam width selection has focused on inference efficiency \citep{qin2025dsbd, freita…
Who Said Neural Networks Aren't Linear?
Nimrod Berman, Assaf Hallak, Assaf Shocher
Neural networks are famously nonlinear. However, linearity is defined relative to a pair of vector spaces, . Leveraging the algebraic concept of transport of structure,…
From Actions to Words: Towards Abstractive-Textual Policy Summarization in RL
Sahar Admoni, Assaf Hallak, Yftah Ziser +2
Explaining reinforcement learning agents is challenging because policies emerge from complex reward structures and neural representations that are difficult for humans to interpret…
RL-RC-DoT: A Block-level RL agent for Task-Aware Video Compression
Uri Gadot, Assaf Shocher, Shie Mannor +2
Video encoders optimize compression for human perception by minimizing reconstruction error under bit-rate constraints. In many modern applications such as autonomous driving, an o…
Policy Gradient with Tree Expansion
Gal Dalal, Assaf Hallak, Gugan Thoppe +2
Policy gradient methods are notorious for having a large variance and high sample complexity. To mitigate this, we introduce SoftTreeMax -- a generalization of softmax that employs…
SoftTreeMax: Policy Gradient with Tree Search
Gal Dalal, Assaf Hallak, Shie Mannor +1
Policy-gradient methods are widely used for learning control policies. They can be easily distributed to multiple workers and reach state-of-the-art results in many domains. Unfort…
Reinforcement Learning with a Terminator
Guy Tennenholtz, Nadav Merlis, Lior Shani +5
We present the problem of reinforcement learning with exogenous termination. We define the Termination Markov Decision Process (TerMDP), an extension of the MDP framework, in which…
Planning and Learning with Adaptive Lookahead
Aviv Rosenberg, Assaf Hallak, Shie Mannor +2
Some of the most powerful reinforcement learning frameworks use planning for action selection. Interestingly, their planning horizon is either fixed or determined arbitrarily by th…
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning
Guy Tennenholtz, Assaf Hallak, Gal Dalal +3
We consider the problem of using expert data with unobserved confounders for imitation and reinforcement learning. We begin by defining the problem of learning from confounded expe…