5 papers
LLM Program Optimization via Retrieval Augmented Search
Sagnik Anupam, Alexander Shypula, Osbert Bastani
Recent work has demonstrated the potential of large language models (LLMs) for program optimization, a key challenge in programming languages. We propose a blackbox adaptation meth…
BrowserArena: Evaluating LLM Agents on Real-World Web Navigation Tasks
Sagnik Anupam, Davis Brown, Shuo Li +3
LLM web agents now browse and take actions on the open web, yet current agent evaluations are constrained to sandboxed environments or artificial tasks. We introduce BrowserArena,…
RAPID: An Efficient Reinforcement Learning Algorithm for Small Language Models
Lianghuan Huang, Sagnik Anupam, Insup Lee +2
Reinforcement learning (RL) has emerged as a promising strategy for finetuning small language models (SLMs) to solve targeted tasks such as math and coding. However, RL algorithms…
Effective Reinforcement Learning for Reasoning in Language Models
Lianghuan Huang, Shuo Li, Sagnik Anupam +2
Reinforcement learning (RL) has emerged as a promising strategy for improving the reasoning capabilities of language models (LMs) in domains such as mathematics and coding. However…
MathDSL: A Domain-Specific Language for Concise Mathematical Solutions Via Program Synthesis
Sagnik Anupam, Maddy Bowers, Omar Costilla-Reyes +1
We present MathDSL, a Domain-Specific Language (DSL) for mathematical equation solving, which, when deployed in program synthesis models, outperforms state-of-the-art reinforcement…