1 citations · 1 across the 10 of their papers we have counts for
10 papers
MNIST-PRO: MNIST is Back as a Partially Observable World for AI Agents
Vernon Toh, Navonil Majumder, Zhengyuan Liu +2
AI agents in partially observable environments need to coordinate active sensing with working memory to maintain an evolving perceptual state. However, existing benchmarks struggle…
ScrambleToolBench: Agents Search Exhaustively Even When Their Own Map Points to the Next Step
Vernon Toh, Navonil Majumder, Zhengyuan Liu +2
To operate robustly in open-world environments, autonomous agents should be able to infer the behavior of unfamiliar systems through interaction alone, even in the absence of docum…
GRAIL: Gradient-Reweighted Advantages for Reinforcement Learning with Verifiable Rewards
Tej Deep Pala, Vernon Toh, Soujanya Poria
Reinforcement learning with verifiable rewards (e.g. GRPO) is now a common way to improve mathematical reasoning in Large Language Models (LLMs). However, current methods usually b…
Training Vision-Language Process Reward Models for Test-Time Scaling in Multimodal Reasoning: Key Insights and Lessons Learned
Brandon Ong, Tej Deep Pala, Vernon Toh +2
Process Reward Models (PRMs) provide step-level supervision that improves the reliability of reasoning in large language models. While PRMs have been extensively studied in text-ba…
Lessons from Training Grounded LLMs with Verifiable Rewards
Shang Hong Sim, Tej Deep Pala, Vernon Toh +5
Generating grounded and trustworthy responses remains a key challenge for large language models (LLMs). While retrieval-augmented generation (RAG) with citation-based grounding hol…
The Jumping Reasoning Curve? Tracking the Evolution of Reasoning Performance in GPT-[n] and o-[n] Models on Multimodal Puzzles
Vernon Y. H. Toh, Yew Ken Chia, Deepanway Ghosal +1
The releases of OpenAI's o-[n] series, such as o1, o3, and o4-mini, mark a significant paradigm shift in Large Language Models towards advanced reasoning capabilities. Notably, mod…
Emma-X: An Embodied Multimodal Action Model with Grounded Chain of Thought and Look-ahead Spatial Reasoning
Qi Sun, Pengfei Hong, Tej Deep Pala +4
Traditional reinforcement learning-based robotic control methods are often task-specific and fail to generalize across diverse environments or unseen objects and instructions. Visu…
Not All Votes Count! Programs as Verifiers Improve Self-Consistency of Language Models for Math Reasoning
Vernon Y. H. Toh, Deepanway Ghosal, Soujanya Poria
Large language models (LLMs) have shown increasing competence in solving mathematical reasoning problems. However, many open-source LLMs still struggle with errors in calculation a…
Ferret: Faster and Effective Automated Red Teaming with Reward-Based Scoring Technique
Tej Deep Pala, Vernon Y. H. Toh, Rishabh Bhardwaj +1
In today's era, where large language models (LLMs) are integrated into numerous real-world applications, ensuring their safety and robustness is crucial for responsible AI usage. A…
VerityMath: Advancing Mathematical Reasoning by Self-Verification Through Unit Consistency
Vernon Toh Yan Han, Ratish Puduppully, Nancy F. Chen
Large Language Models (LLMs), combined with program-based solving techniques, are increasingly demonstrating proficiency in mathematical reasoning. For example, closed-source model…