1 citations · 1 across the 7 of their papers we have counts for
7 papers
FrogNano: Training a 4B Coding Agent via Online Task Synthesis
Minseon Kim, Zhengyan Shi, Emiliano Penaloza +14
We present FrogNano, a 4B coding agent designed to tackle software engineering (SWE) tasks efficiently and effectively, even under resource-constrained environments. It is post-tra…
Dialectics of Alignment: Harnessing Unsafe Knowledge for Dynamic Safety Routing
Maryam Hashemzadeh, Jerry Huang, Minseon Kim +2
The prevailing paradigm in large language model (LLM) alignment operates via erasure, filtering unsafe data or training models to strictly refuse harmful prompts. While effective a…
Probabilistic Calibration Is a Trainable Capability in Language Models
Davide Baldelli, Sruthi Kuriakose, Maryam Hashemzadeh +2
Language models are increasingly used in settings where outputs should follow an intended distribution, yet their generation probabilities are often poorly calibrated to it. In man…
REAM: Merging Improves Pruning of Experts in LLMs
Saurav Jha, Maryam Hashemzadeh, Ali Saheb Pasand +3
Mixture-of-Experts (MoE) large language models (LLMs) are among the top-performing architectures. The largest models, often with hundreds of billions of parameters, pose significan…
Sub-goal Distillation: A Method to Improve Small Language Agents
Maryam Hashemzadeh, Elias Stengel-Eskin, Sarath Chandar +1
While Large Language Models (LLMs) have demonstrated significant promise as agents in interactive tasks, their substantial computational requirements and restricted number of calls…
From Language to Language-ish: How Brain-Like is an LSTM's Representation of Nonsensical Language Stimuli?
Maryam Hashemzadeh, Greta Kaufeld, Martha White +2
The representations generated by many models of language (word embeddings, recurrent neural networks and transformers) correlate to brain activity recorded while people read. Howev…
Exploiting generalization in the subspaces for faster model-based learning
Maryam Hashemzadeh, Reshad Hosseini, Majid Nili Ahmadabadi
Due to the lack of enough generalization in the state-space, common methods in Reinforcement Learning (RL) suffer from slow learning speed especially in the early learning trials.…