3 citations · 6 across the 10 of their papers we have counts for
10 papers
Mid-Training with Self-Generated Data Improves Reinforcement Learning in Language Models
Aswin RRV, Jacob Dineen, Divij Handa +4
The effectiveness of Reinforcement Learning (RL) in Large Language Models (LLMs) depends on the nature and diversity of the data used before and during RL. In particular, reasoning…
Vocabulary Dropout for Curriculum Diversity in LLM Co-Evolution
Jacob Dineen, Aswin RRV, Zhikun Xu +1
Co-evolutionary self-play, where one language model generates problems and another solves them, promises curriculum learning without human supervision. The promise breaks down earl…
PHANTOM RECALL: When Familiar Puzzles Fool Smart Models
Souradeep Mukhopadhyay, Rishabh Baral, Nimeesh Mahajan +5
Large language models (LLMs) such as GPT, Gemini, and Claude often appear adept at solving classic logic puzzles--but how much genuine reasoning underlies their answers? Recent evi…
GuidedSampling: Steering LLMs Towards Diverse Candidate Solutions at Inference-Time
Divij Handa, Mihir Parmar, Aswin RRV +3
Repeated Sampling (RS) is a simple inference-time algorithm that has been shown to improve model performance on complex tasks. Although it is an effective way of scaling inference…
ThinkTuning: Instilling Cognitive Reflections without Distillation
Aswin RRV, Jacob Dineen, Divij Handa +4
Recent advances in test-time scaling have led to the emergence of thinking LLMs that exhibit self-reflective behaviors and multi-step reasoning. While RL drives this self-improveme…
QA-LIGN: Aligning LLMs through Constitutionally Decomposed QA
Jacob Dineen, Aswin RRV, Qin Liu +8
Alignment of large language models (LLMs) with principles like helpfulness, honesty, and harmlessness typically relies on scalar rewards that obscure which objectives drive the tra…
ToW: Thoughts of Words Improve Reasoning in Large Language Models
Zhikun Xu, Ming Shen, Jacob Dineen +6
We introduce thoughts of words (ToW), a novel training-time data-augmentation method for next-word prediction. ToW views next-word prediction as a core reasoning task and injects f…
Step-by-Step Reasoning to Solve Grid Puzzles: Where do LLMs Falter?
Nemika Tyagi, Mihir Parmar, Mohith Kulkarni +5
Solving grid puzzles involves a significant amount of logical reasoning. Hence, it is a good domain to evaluate the reasoning capability of a model which can then guide us to impro…
Chaos with Keywords: Exposing Large Language Models Sycophantic Hallucination to Misleading Keywords and Evaluating Defense Strategies
Aswin RRV, Nemika Tyagi, Md Nayem Uddin +2
This study explores the sycophantic tendencies of Large Language Models (LLMs), where these models tend to provide answers that match what users want to hear, even if they are not…
Triple Preference Optimization: Achieving Better Alignment using a Single Step Optimization
Amir Saeidi, Shivanshu Verma, Aswin RRV +2
Reinforcement Learning with Human Feedback (RLHF) enhances the alignment of Large Language Models (LLMs). However, its limitations have led to the development of Direct Preference…