33 citations · 35 across the 8 of their papers we have counts for
9 papers
AgentHorizon: Evaluating Agentic Judges for Long-Horizon Computer-Use Tasks
Xing Han Lù, Dheeraj Vattikonda, Sina Hajimiri +7
Computer-use agents are capable of completing complex tasks, increasing the use of automatic judges to determine success, either for training or for evaluation without human involv…
The Markovian Thinker: Architecture-Agnostic Linear Scaling of Reasoning
Milad Aghajohari, Kamran Chitsaz, Amirhossein Kazemnejad +4
Reinforcement learning (RL) has recently become a strong recipe for training reasoning LLMs that produce long chains of thought (LongCoT). Yet the standard RL "thinking environment…
The Promise of RL for Autoregressive Image Editing
Saba Ahmadi, Rabiul Awal, Ankur Sikarwar +8
While image generation techniques are now capable of producing high-quality images that respect prompts which span multiple sentences, the task of text-guided image editing remains…
AgentRewardBench: Evaluating Automatic Evaluations of Web Agent Trajectories
Xing Han Lù, Amirhossein Kazemnejad, Nicholas Meade +7
Web agents enable users to perform tasks on web browsers through natural language interaction. Evaluating web agents trajectories is an important problem, since it helps us determi…
DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning
Sara Vera Marjanović, Arkil Patel, Vaibhav Adlakha +14
Large Reasoning Models like DeepSeek-R1 mark a fundamental shift in how LLMs approach complex problems. Instead of directly producing an answer for a given input, DeepSeek-R1 creat…
VinePPO: Refining Credit Assignment in RL Training of LLMs
Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance +4
Large language models (LLMs) are increasingly applied to complex reasoning tasks that require executing several complex steps before receiving any reward. Properly assigning credit…
Measuring the Knowledge Acquisition-Utilization Gap in Pretrained Language Models
Amirhossein Kazemnejad, Mehdi Rezagholizadeh, Prasanna Parthasarathi +1
While pre-trained language models (PLMs) have shown evidence of acquiring vast amounts of knowledge, it remains unclear how much of this parametric knowledge is actually usable in…
The Impact of Positional Encoding on Length Generalization in Transformers
Amirhossein Kazemnejad, Inkit Padhi, Karthikeyan Natesan Ramamurthy +2
Length generalization, the ability to generalize from small training context sizes to larger ones, is a critical challenge in the development of Transformer-based language models.…
The Curious Case of Absolute Position Embeddings
Koustuv Sinha, Amirhossein Kazemnejad, Siva Reddy +3
Transformer language models encode the notion of word order using positional information. Most commonly, this positional information is represented by absolute position embeddings…