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Daman Arora

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4 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Training Language Models to Reason Efficiently

Daman Arora
Andrea Zanette

Scaling model size and training data has led to great advances in the performance of Large Language Models (LLMs). However, the diminishing returns of this approach necessitate alternative methods to improve model capabilities, particularly in tasks requiring advanced reasoning. Large reasoning models, which leverage long chain-of-thoughts, bring unprecedented breakthroughs in problem-solving capabilities but at a substantial deployment cost associated to longer generations. Reducing inference costs is crucial for the economic feasibility, user experience, and environmental sustainability of these models. In this work, we propose to train large reasoning models to reason efficiently. Our method incentivizes models to minimize unnecessary computational overhead while largely maintaining accuracy, thereby achieving substantial deployment efficiency gains. It enables the derivation of a family of reasoning models with varying efficiency levels, controlled via a single hyperparameter. Experiments on two open-weight large reasoning models demonstrate significant reductions in inference cost while preserving most of the accuracy.

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UAI Conference 2023 Conference Paper

SymNet 3. 0: Exploiting Long-Range Influences in Learning Generalized Neural Policies for Relational MDPs

Vishal Sharma
Daman Arora
Mausam
Parag Singla

We focus on the learning of generalized neural policies for Relational Markov Decision Processes (RMDPs) expressed in RDDL. Recent work first converts the instances of a relational domain into an instance graph, and then trains a Graph Attention Network (GAT) of fixed depth with parameters shared across instances to learn a state representation, which can be decoded to get the policy [sharma et al. , 22]. Unfortunately, this approach struggles to learn policies that exploit long-range dependencies – a fact we formally prove in this paper. As a remedy, we first construct a novel influence graph characterized by edges capturing one-step influence (dependence) between nodes based on the transition model. We then define influence distance between two nodes as the shortest path between them in this graph – a feature we exploit to represent long-range dependencies. We show that our architecture, referred to as Symbolic Influence Network (SymNet3. 0), with its distance-based features, does not suffer from the representational issues faced by earlier approaches. Extensive experimentation demonstrates that we are competitive with existing baselines on 12 standard IPPC domains, and perform significantly better on six additional domains (including IPPC variants), designed to test a model’s capability in capturing long-range dependencies. Further analysis shows that SymNet3. 0 automatically learns to focus on nodes that have key information for representing policies that capture long-range dependencies.

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PRL Workshop 2023 Workshop Paper

SymNet 3.0: Exploiting Long-Range Influences in Learning Generalized Neural Policies for Relational MDPs

Vishal Sharma
Daman Arora
Mausam
Parag Singla

We focus on the learning of generalized neural policies for Relational Markov Decision Processes (RMDPs) expressed in RDDL. Recent work first converts the instances of a relational domain into an instance graph, and then trains a Graph Attention Network (GAT) of fixed depth with parameters shared across instances to learn a state representation, which can be decoded to get the policy [sharma et al., 22]. Unfortunately, this approach struggles to learn policies that exploit long-range dependencies – a fact we formally prove in this paper. As a remedy, we first construct a novel influence graph characterized by edges capturing one-step influence (dependence) between nodes based on the transition model. We then define influence distance between two nodes as the shortest path between them in this graph – a feature we exploit to represent long-range dependencies. We show that our architecture, referred to as Symbolic Influence Network (SymNet3.0), with its distance-based features, does not suffer from the representational issues faced by earlier approaches. Extensive experimentation demonstrates that we are competitive with existing baselines on 12 standard IPPC domains, and perform significantly better on six additional domains (including IPPC variants), designed to test a model’s capability in capturing long-range dependencies. Further analysis shows that SymNet3.0 automatically learns to focus on nodes that have key information for representing policies that capture long-range dependencies.

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UAI Conference 2022 Conference Paper

SymNet 2. 0: Effectively handling Non-Fluents and Actions in Generalized Neural Policies for RDDL Relational MDPs

Vishal Sharma
Daman Arora
Florian Geißer
Mausam
Parag Singla

Relational MDPs (RMDPs) compactly represent an infinite set of MDPs with an unbounded number of objects. Solving an RMDP requires a generalized policy that applies to all instances of a domain. Recently, Garg et al. proposed SymNet for this task– it constructs a graph neural network that shares parameters across all instances in a domain, thus making it applicable to any instance in a zero-shot manner. Our analysis of SymNet reveals that it performs no better than random on 1/4th of planning competition domains. The key reasons are its design choices: it misses important information during graph construction, leading to (1) poor generalizability, and (2) potential non-identifiability of different actions. In response, our solution, SymNet2. 0, substantially augments SymNet’s graph construction approach by introducing additional nodes and edges which allow a better transfer of important information about a domain. It also improves SymNet’s action decoders with relevant information from objects to make different actions identifiable during scoring. Extensive experiments on twelve competition domains, where we use imitation learning over data generated from the PROST planner, demonstrate that SymNet2. 0 performs vastly better than SymNet. Interestingly, even though SymNet2. 0 is trained over data from PROST, it outperforms the planner on several test instances due to former’s ability to scale to large instances in a zero-shot manner.

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