38 citations · 63 across the 19 of their papers we have counts for
21 papers
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,…
Aligning What LLMs Do and Say: Towards Self-Consistent Explanations
Sahar Admoni, Ofra Amir, Assaf Hallak +1
Large language models (LLMs) seem to offer an easy path to interpretability: just ask them to explain their answers. Yet the features driving an answer often differ from those emph…
"Trust me on this" Explaining Agent Behavior to a Human Terminator
Uri Menkes, Assaf Hallak, Ofra Amir
Consider a setting where a pre-trained agent is operating in an environment and a human operator can decide to temporarily terminate its operation and take-over for some duration o…
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…
PlaMo: Plan and Move in Rich 3D Physical Environments
Assaf Hallak, Gal Dalal, Chen Tessler +3
Controlling humanoids in complex physically simulated worlds is a long-standing challenge with numerous applications in gaming, simulation, and visual content creation. In our setu…
On the Products of Stochastic and Diagonal Matrices
Assaf Hallak, Gal Dalal
Consider a stochastic matrix and diagonal matrix In this work, we introduce Tilted matrices. A Tilted matrix is the product , where is a diagonal normalization…
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…
Improve Agents without Retraining: Parallel Tree Search with Off-Policy Correction
Assaf Hallak, Gal Dalal, Steven Dalton +3
Tree Search (TS) is crucial to some of the most influential successes in reinforcement learning. Here, we tackle two major challenges with TS that limit its usability: \textit{dist…
Automatic Representation for Lifetime Value Recommender Systems
Assaf Hallak, Yishay Mansour, Elad Yom-Tov
Many modern commercial sites employ recommender systems to propose relevant content to users. While most systems are focused on maximizing the immediate gain (clicks, purchases or…
Consistent On-Line Off-Policy Evaluation
Assaf Hallak, Shie Mannor
The problem of on-line off-policy evaluation (OPE) has been actively studied in the last decade due to its importance both as a stand-alone problem and as a module in a policy impr…
Generalized Emphatic Temporal Difference Learning: Bias-Variance Analysis
Assaf Hallak, Aviv Tamar, Remi Munos +1
We consider the off-policy evaluation problem in Markov decision processes with function approximation. We propose a generalization of the recently introduced \emph{emphatic tempor…
Emphatic TD Bellman Operator is a Contraction
Assaf Hallak, Aviv Tamar, Shie Mannor
Recently, \citet{SuttonMW15} introduced the emphatic temporal differences (ETD) algorithm for off-policy evaluation in Markov decision processes. In this short note, we show that t…
Off-policy evaluation for MDPs with unknown structure
Assaf Hallak, François Schnitzler, Timothy Mann +1
Off-policy learning in dynamic decision problems is essential for providing strong evidence that a new policy is better than the one in use. But how can we prove superiority withou…
Contextual Markov Decision Processes
Assaf Hallak, Dotan Di Castro, Shie Mannor
We consider a planning problem where the dynamics and rewards of the environment depend on a hidden static parameter referred to as the context. The objective is to learn a strateg…
How to sample if you must: on optimal functional sampling
Assaf Hallak, Shie Mannor
We examine a fundamental problem that models various active sampling setups, such as network tomography. We analyze sampling of a multivariate normal distribution with an unknown e…