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Abhishek Bhandwaldar

8 accepted papers

2026

Sculpting Subspaces: Constrained Full Fine-Tuning in LLMs for Continual Learning

ICLR 2026poster

Continual learning in large language models (LLMs) is prone to catastrophic forgetting, where adapting to new tasks significantly degrades performance on previously learned ones. Existing parameter-efficient methods often limit model expressivity or introduce new parameters per task, creating scalab…

Cited by 0SourcecodeScholar
2025

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods

NeurIPS 2025poster

Large language models (LLMs) have achieved significant performance gains via scaling up model sizes and/or data. However, recent evidence suggests diminishing returns from such approaches, motivating a pivot to scaling test-time compute. Existing deterministic inference-time scaling methods, usuall…

Cited by 0SourceScholar
2025

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs

ICLR 2025poster

The rise of large language models (LLMs) has created a significant disparity: industrial research labs with their computational resources, expert teams, and advanced infrastructures, can effectively fine-tune LLMs, while individual developers and small organizations face barriers due to limited reso…

2023

Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets

NeurIPS 2023poster

Offline reinforcement learning (RL) enables learning a decision-making policy without interaction with the environment. This makes it particularly beneficial in situations where such interactions are costly. However, a known challenge for offline RL algorithms is the distributional mismatch between…

2022

The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark Towards Physically Realistic Embodied AI

ICRA 2022poster

We introduce a visually-guided task-and-motion planning benchmark, which we call the ThreeDWorld Trans-port Challenge. In this challenge, an embodied agent is spawned randomly in a simulated physical home environment and required to transport a small set of objects scattered around the house with co…

Cited by 46SourceScholar
2021

AGENT: A Benchmark for Core Psychological Reasoning

ICML 2021spotlight

For machine agents to successfully interact with humans in real-world settings, they will need to develop an understanding of human mental life. Intuitive psychology, the ability to reason about hidden mental variables that drive observable actions, comes naturally to people: even pre-verbal infants…

Cited by 96SourcePDFScholar
2021

OPEn: An Open-ended Physics Environment for Learning Without a Task

IROS 2021poster

Humans have mental models that allow them to plan, experiment, and reason in the physical world. How should an intelligent agent go about learning such models? In this paper, we will study if models of the world learned in an open-ended physics environment, without any specific tasks, can be reused…

Cited by 3SourceScholar
2021

ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation

NeurIPS 2021poster

We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation. TDW enables the simulation of high-fidelity sensory data and physical interactions between mobile agents and objects in rich 3D environments. Unique properties include real-time near-photo-realistic image ren…

Cited by 342SourcecodeScholar