3 citations · 3 across the 6 of their papers we have counts for
6 papers
RLSR: Reinforcement Learning from Self Reward
Toby Simonds, Kevin Lopez, Akira Yoshiyama +1
Large language models can generate solutions to complex problems, but training them with reinforcement learning typically requires verifiable rewards that are expensive to create a…
LLMs for Engineering: Teaching Models to Design High Powered Rockets
Toby Simonds
Large Language Models (LLMs) have transformed software engineering, but their application to physical engineering domains remains underexplored. This paper evaluates LLMs' capabili…
LADDER: Self-Improving LLMs Through Recursive Problem Decomposition
Toby Simonds, Akira Yoshiyama
We introduce LADDER (Learning through Autonomous Difficulty-Driven Example Recursion), a framework which enables Large Language Models to autonomously improve their problem-solving…
Entropy Adaptive Decoding: Dynamic Model Switching for Efficient Inference
Toby Simonds
We present Entropy Adaptive Decoding (EAD), a novel approach for efficient language model inference that dynamically switches between different-sized models based on prediction unc…
REL: Working out is all you need
Toby Simonds, Jey Han Lau, Chaithanya Bandi
Recent developments, particularly OpenAI's O1 model, have demonstrated the remarkable potential of Large Language Models (LLMs) for complex reasoning tasks. Through analysis of O1'…
MoDEM: Mixture of Domain Expert Models
Toby Simonds, Kemal Kurniawan, Jey Han Lau
We propose a novel approach to enhancing the performance and efficiency of large language models (LLMs) by combining domain prompt routing with domain-specialized models. We introd…