16 citations · 40 across the 13 of their papers we have counts for
5 papers · 1 filter
LLMStinger: Jailbreaking LLMs using RL fine-tuned LLMs
Piyush Jha, Arnav Arora, Vijay Ganesh
We introduce LLMStinger, a novel approach that leverages Large Language Models (LLMs) to automatically generate adversarial suffixes for jailbreak attacks. Unlike traditional metho…
RLSF: Fine-tuning LLMs via Symbolic Feedback
Piyush Jha, Prithwish Jana, Pranavkrishna Suresh +2
Large Language Models (LLMs) have transformed AI but often struggle with tasks that require domain-specific reasoning and logical alignment. Traditional fine-tuning methods do not…
A Reinforcement Learning based Reset Policy for CDCL SAT Solvers
Chunxiao Li, Charlie Liu, Jonathan Chung +3
Restart policy is an important technique used in modern Conflict-Driven Clause Learning (CDCL) solvers, wherein some parts of the solver state are erased at certain intervals durin…
Layered and Staged Monte Carlo Tree Search for SMT Strategy Synthesis
Zhengyang Lu, Stefan Siemer, Piyush Jha +3
Modern SMT solvers, such as Z3, offer user-controllable strategies, enabling users to tailor solving strategies for their unique set of instances, thus dramatically enhancing solve…
AlphaMapleSAT: An MCTS-based Cube-and-Conquer SAT Solver for Hard Combinatorial Problems
Piyush Jha, Zhengyu Li, Zhengyang Lu +3
This paper introduces AlphaMapleSAT, a Cube-and-Conquer (CnC) parallel SAT solver that integrates Monte Carlo Tree Search (MCTS) with deductive feedback to efficiently solve challe…