# Problem Decomposition

9 papers with code • 0 benchmarks • 0 datasets

## Benchmarks

These leaderboards are used to track progress in Problem Decomposition
## Most implemented papers

# Meta-Optimizing Semantic Evolutionary Search

I present MOSES (meta-optimizing semantic evolutionary search), a new probabilistic modeling (estimation of distribution) approach to program evolution.

# Decomposition Methods with Deep Corrections for Reinforcement Learning

In contexts where an agent interacts with multiple entities, utility decomposition can be used to separate the global objective into local tasks considering each individual entity independently.

# Learning Reward Machines for Partially Observable Reinforcement Learning

Reward Machines (RMs), originally proposed for specifying problems in Reinforcement Learning (RL), provide a structured, automata-based representation of a reward function that allows an agent to decompose problems into subproblems that can be efficiently learned using off-policy learning.

# Fast reinforcement learning with generalized policy updates

Both strategies considerably reduce the amount of data needed to solve a reinforcement-learning problem.

# Distilling Reasoning Capabilities into Smaller Language Models

In this work, we propose an alternative reasoning scheme, Socratic CoT, that learns a decomposition of the original problem into a sequence of subproblems and uses it to guide the intermediate reasoning steps.

# Comparison of Model-Free and Model-Based Learning-Informed Planning for PointGoal Navigation

In this work, we compare the state-of-the-art Deep Reinforcement Learning based approaches with Partially Observable Markov Decision Process (POMDP) formulation of the point goal navigation problem.

# Rolling Horizon based Temporal Decomposition for the Offline Pickup and Delivery Problem with Time Windows

In smaller problem instances, the baseline approach is as competitive as our framework.

# Fast Matrix Multiplication Without Tears: A Constraint Programming Approach

In this work, we propose a simple yet novel Constraint Programming approach to find non-commutative algorithms for fast matrix multiplication or provide proof of infeasibility otherwise.

# Small Language Models Fine-tuned to Coordinate Larger Language Models improve Complex Reasoning

Additionally, we show that DaSLaM is not limited by the solver's capabilities as a function of scale; e. g., solver LMs with diverse sizes give significant performance improvement with our solver-agnostic decomposition technique.