11 citations · 14 across the 11 of their papers we have counts for
11 papers
Reinforcement Learning from Rich Feedback with Distributional DAgger
Rishabh Agrawal, Jacob Fein-Ashley, Paria Rashidinejad
Reasoning models have advanced rapidly, but the dominant reinforcement learning from verifiable rewards (RLVR) recipe remains surprisingly narrow: sample many responses and reward…
Solve the Loop: Attractor Models for Language and Reasoning
Jacob Fein-Ashley, Paria Rashidinejad
Looped Transformers offer a promising alternative to purely feed-forward computation by iteratively refining latent representations, improving language modeling and reasoning. Yet…
Reuse your FLOPs: Scaling RL on Hard Problems by Conditioning on Very Off-Policy Prefixes
Amrith Setlur, Zijian Wang, Andrew Cohen +2
Typical reinforcement learning (RL) methods for LLM reasoning waste compute on hard problems, where correct on-policy traces are rare, policy gradients vanish, and learning stalls.…
CrispEdit: Low-Curvature Projections for Scalable Non-Destructive LLM Editing
Zarif Ikram, Arad Firouzkouhi, Stephen Tu +2
A central challenge in large language model (LLM) editing is capability preservation: methods that successfully change targeted behavior can quietly game the editing proxy and corr…
SPG: Sandwiched Policy Gradient for Masked Diffusion Language Models
Chenyu Wang, Paria Rashidinejad, DiJia Su +9
Diffusion large language models (dLLMs) are emerging as an efficient alternative to autoregressive models due to their ability to decode multiple tokens in parallel. However, align…
Sail into the Headwind: Alignment via Robust Rewards and Dynamic Labels against Reward Hacking
Paria Rashidinejad, Yuandong Tian
Aligning AI systems with human preferences typically suffers from the infamous reward hacking problem, where optimization of an imperfect reward model leads to undesired behaviors.…
Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning
Hanlin Zhu, Paria Rashidinejad, Jiantao Jiao
We propose A-Crab (Actor-Critic Regularized by Average Bellman error), a new practical algorithm for offline reinforcement learning (RL) in complex environments with insufficient d…
Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian
Paria Rashidinejad, Hanlin Zhu, Kunhe Yang +2
Offline reinforcement learning (RL), which refers to decision-making from a previously-collected dataset of interactions, has received significant attention over the past years. Mu…
MADE: Exploration via Maximizing Deviation from Explored Regions
Tianjun Zhang, Paria Rashidinejad, Jiantao Jiao +3
In online reinforcement learning (RL), efficient exploration remains particularly challenging in high-dimensional environments with sparse rewards. In low-dimensional environments,…
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism
Paria Rashidinejad, Banghua Zhu, Cong Ma +2
Offline (or batch) reinforcement learning (RL) algorithms seek to learn an optimal policy from a fixed dataset without active data collection. Based on the composition of the offli…
SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term Memory
Paria Rashidinejad, Jiantao Jiao, Stuart Russell
We present an efficient and practical (polynomial time) algorithm for online prediction in unknown and partially observed linear dynamical systems (LDS) under stochastic noise. Whe…