1 citations · 5 across the 9 of their papers we have counts for
6 papers · 1 filter
Eat Your Precision: Precision Objective Mitigates Reward Hacking in Rubric-based RL
Hyun Ryu, Jong-Heon Kim, Yiqing Xie +3
Rubric-based rewards enable effective reinforcement learning on non-verifiable tasks. However, because these rewards are typically defined as a weighted measure of rubric coverage,…
ReaComp: Compiling LLM Reasoning into Symbolic Solvers for Efficient Program Synthesis
Atharva Naik, Yash Mathur, Prakam +2
LLMs can solve program synthesis tasks but remain inefficient and unreliable on hard instances requiring large combinatorial search. Given a small set of reasoning traces, we use c…
PBEBench: A Multi-Step Programming by Examples Reasoning Benchmark inspired by Historical Linguistics
Atharva Naik, Prakam, Yash Mathur +6
Although many benchmarks evaluate the reasoning abilities of Large Language Models (LLMs) within domains such as mathematics, coding, or data wrangling, few abstract away from doma…
RepoST: Scalable Repository-Level Coding Environment Construction with Sandbox Testing
Yiqing Xie, Alex Xie, Divyanshu Sheth +3
We present RepoST, a scalable method to construct environments that provide execution feedback for repository-level code generation for both training and evaluation. Unlike existin…
Programming by Examples Meets Historical Linguistics: A Large Language Model Based Approach to Sound Law Induction
Atharva Naik, Darsh Agrawal, Hong Sng +8
Historical linguists have long written "programs" that convert reconstructed words in an ancestor language into their attested descendants via ordered string rewrite functions (cal…
Data Augmentation for Code Translation with Comparable Corpora and Multiple References
Yiqing Xie, Atharva Naik, Daniel Fried +1
One major challenge of translating code between programming languages is that parallel training data is often limited. To overcome this challenge, we present two data augmentation…