Skill Expansion and Composition in Parameter Space | Paper Citation Record

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This paper cites Road” encompasses three levels of difficulty for self-driving cars: easy, medium, and hard, while “Vehicle.

Skill Expansion and Composition in Parameter Space Road” encompasses three levels of difficulty for self-driving cars: easy, medium, and hard, while “Vehicle

Reference 1

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Skill Expansion and Composition in Parameter Space Unresolved cited work

Reference 3

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Observation 1e34ce56-c691-4043-9a86-7da0dad1c6e4 · outbound

This paper cites Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling.

Skill Expansion and Composition in Parameter Space Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling

Reference 4

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This paper cites Modularized skills for multitask learning.

Skill Expansion and Composition in Parameter Space Modularized skills for multitask learning

Reference 5

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Observation 8f71172d-919a-4c9a-a7ca-e21d2eb4ba34 · outbound

This paper cites Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?.

Skill Expansion and Composition in Parameter Space Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?

Reference 6

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Reference 7

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Observation bdd6247c-8b15-43a3-a745-36ce58d3e245 · outbound

This paper cites D4RL: Datasets for Deep Data-Driven Reinforcement Learning.

Skill Expansion and Composition in Parameter Space D4RL: Datasets for Deep Data-Driven Reinforcement Learning

Reference 8

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Reference 9

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Observation d2af2f35-d891-4602-94ef-bb549ad3d3b9 · outbound

This paper cites IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies.

Skill Expansion and Composition in Parameter Space IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies

Reference 10

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This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Skill Expansion and Composition in Parameter Space LoRA: Low-Rank Adaptation of Large Language Models

Reference 11

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This paper cites Solving Continual Offline Reinforcement Learning with Decision Transformer.

Skill Expansion and Composition in Parameter Space Solving Continual Offline Reinforcement Learning with Decision Transformer

Reference 13

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This paper cites Unsupervised-to-Online Reinforcement Learning.

Skill Expansion and Composition in Parameter Space Unsupervised-to-Online Reinforcement Learning

Reference 14

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This paper cites Adam: A Method for Stochastic Optimization.

Skill Expansion and Composition in Parameter Space Adam: A Method for Stochastic Optimization

Reference 15

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This paper cites Daniel Lawson and Ahmed H Qureshi.

Skill Expansion and Composition in Parameter Space Daniel Lawson and Ahmed H Qureshi

Reference 16

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This paper cites Datasets and Benchmarks for Offline Safe Reinforcement Learning.

Skill Expansion and Composition in Parameter Space Datasets and Benchmarks for Offline Safe Reinforcement Learning

Reference 19

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This paper cites Highly Efficient Self-Adaptive Reward Shaping for Reinforcement Learning.

Skill Expansion and Composition in Parameter Space Highly Efficient Self-Adaptive Reward Shaping for Reinforcement Learning

Reference 20

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This paper cites Meta-neural networks that learn by learning.

Skill Expansion and Composition in Parameter Space Meta-neural networks that learn by learning

Reference 21

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This paper cites 6892–6903.

Skill Expansion and Composition in Parameter Space 6892–6903

Reference 23

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This paper cites Modular deep belief networks that do not forget.

Skill Expansion and Composition in Parameter Space Modular deep belief networks that do not forget

Reference 24

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Observation 3409346e-700e-43ee-a3c2-7846a08962f7 · outbound

This paper cites LoRA Soups: Merging LoRAs for Practical Skill Composition Tasks.

Skill Expansion and Composition in Parameter Space LoRA Soups: Merging LoRAs for Practical Skill Composition Tasks

Reference 26

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This paper cites Diffusion Policy Policy Optimization.

Skill Expansion and Composition in Parameter Space Diffusion Policy Policy Optimization

Reference 27

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This paper cites An Overview of Multi-Task Learning in Deep Neural Networks.

Skill Expansion and Composition in Parameter Space An Overview of Multi-Task Learning in Deep Neural Networks

Reference 28

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This paper cites Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA.

Skill Expansion and Composition in Parameter Space Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA

Reference 29

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This paper cites An Introduction to Lifelong Supervised Learning.

Skill Expansion and Composition in Parameter Space An Introduction to Lifelong Supervised Learning

Reference 30

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Observation 570fadac-d2d3-42a2-aec1-f2f160ee4624 · outbound

This paper cites Paco: Parameter-compositional multi-task reinforcement learning.

Skill Expansion and Composition in Parameter Space Paco: Parameter-compositional multi-task reinforcement learning

Reference 31

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This paper cites DeepMind Control Suite.

Skill Expansion and Composition in Parameter Space DeepMind Control Suite

Reference 32

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This paper cites Are Expressive Models Truly Necessary for Offline RL?.

Skill Expansion and Composition in Parameter Space Are Expressive Models Truly Necessary for Offline RL?

Reference 34

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This paper cites Multi-task reinforcement learning with soft modularization.

Skill Expansion and Composition in Parameter Space Multi-task reinforcement learning with soft modularization

Reference 35

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This paper cites Trace Norm Regularised Deep Multi-Task Learning.

Skill Expansion and Composition in Parameter Space Trace Norm Regularised Deep Multi-Task Learning

Reference 36

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This paper cites • Assumption on the expressiveness of the pretrain policy.

Skill Expansion and Composition in Parameter Space • Assumption on the expressiveness of the pretrain policy

Reference 39

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Skill Expansion and Composition in Parameter Space Unresolved cited work

Reference 40

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Skill Expansion and Composition in Parameter Space Unresolved cited work

Reference 43

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Skill Expansion and Composition in Parameter Space Unresolved cited work

Reference 44

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This paper cites The modularization method, however, can address this problem fundamen- tally by learning new parameters without disrupting pretrained ones.

Skill Expansion and Composition in Parameter Space The modularization method, however, can address this problem fundamen- tally by learning new parameters without disrupting pretrained ones

Reference 45

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Skill Expansion and Composition in Parameter Space Unresolved cited work

Reference 46

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This paper cites Additionally, Araki et al.

Skill Expansion and Composition in Parameter Space Additionally, Araki et al

Reference 47

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Observation 085c2e6f-b164-4a7c-99d4-273f11098aa1 · outbound

This paper cites For FISOR (Zheng et al., 2024), CDT (Liu et al., 2023b), COptiDICE (Lee et al., 2022a), CPQ (Xu et al.,.

Skill Expansion and Composition in Parameter Space For FISOR (Zheng et al., 2024), CDT (Liu et al., 2023b), COptiDICE (Lee et al., 2022a), CPQ (Xu et al.,

Reference 48

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This paper cites For NSEC and ASEC results, we only change the compositional stages, and meanwhile keep all other training details the same to ensure a fair comparison.

Skill Expansion and Composition in Parameter Space For NSEC and ASEC results, we only change the compositional stages, and meanwhile keep all other training details the same to ensure a fair comparison

Reference 49

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This paper cites (2024) for the policy learning.

Skill Expansion and Composition in Parameter Space (2024) for the policy learning

Reference 50

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Observation c3bc6b9c-c1b9-41ec-a480-c25c9dd7b144 · outbound

This paper cites We compare PSEC with other composition methods NSEC and ASEC, the Scratch method, and the variant PSEC (MLP).

Skill Expansion and Composition in Parameter Space We compare PSEC with other composition methods NSEC and ASEC, the Scratch method, and the variant PSEC (MLP)

Reference 51

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Skill Expansion and Composition in Parameter Space Unresolved cited work

Reference 53

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This paper cites The baseline results for comparison are sourced from the TSRL paper (Cheng et al., 2023), which reports state-of-the-art performance in these regimes.

Skill Expansion and Composition in Parameter Space The baseline results for comparison are sourced from the TSRL paper (Cheng et al., 2023), which reports state-of-the-art performance in these regimes

Reference 54

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This paper cites The training datasets are the same as the datasets collected by L2M.

Skill Expansion and Composition in Parameter Space The training datasets are the same as the datasets collected by L2M

Reference 57

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This paper cites stand,” “walk,.

Skill Expansion and Composition in Parameter Space stand,” “walk,

Reference 59

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This paper cites AWAC: Accelerating Online Reinforcement Learning with Offline Datasets.

Skill Expansion and Composition in Parameter Space AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

Reference 1992

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This paper cites Dota 2 with Large Scale Deep Reinforcement Learning.

Skill Expansion and Composition in Parameter Space Dota 2 with Large Scale Deep Reinforcement Learning

Reference 1997

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This paper cites In a Nutshell, the Human Asked for This: Latent Goals for Following Temporal Specifications.

Skill Expansion and Composition in Parameter Space In a Nutshell, the Human Asked for This: Latent Goals for Following Temporal Specifications

Reference 2006

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This paper cites $ 0.25 ×FrictionSource domain Target domain Thigh Size ×.

Skill Expansion and Composition in Parameter Space $ 0.25 ×FrictionSource domain Target domain Thigh Size ×

Reference 2008

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Observation e90d89e5-4294-4a80-a1e2-2bac7e516751 · outbound

This paper cites The ideal continual learner: An agent that never forgets.

Skill Expansion and Composition in Parameter Space The ideal continual learner: An agent that never forgets

Reference 2011

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Observation b7860f6e-447d-4ea8-b7b0-06377971c912 · outbound

This paper cites Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model.

Skill Expansion and Composition in Parameter Space Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model

Reference 2012

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This paper cites The lines and shaded areas indicate the averages and standard deviations calculated over 5 random seeds.

Skill Expansion and Composition in Parameter Space The lines and shaded areas indicate the averages and standard deviations calculated over 5 random seeds

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:26:10.315122Z

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Observation c9dda185-61fd-4b5c-9954-52164b39632a · outbound

This paper cites Intelligent problem-solving as integrated hierarchical reinforcement learning.

Skill Expansion and Composition in Parameter Space Intelligent problem-solving as integrated hierarchical reinforcement learning

Reference 2017

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Observation 49dbf5f3-12a0-48fa-a614-f4aa52cee14d · outbound

This paper cites Do- main randomization for transferring deep neural networks from simulation to the real world.

Skill Expansion and Composition in Parameter Space Do- main randomization for transferring deep neural networks from simulation to the real world

Reference 2018

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Observation e99eb39b-78fe-49da-a655-12dceeeb9cac · outbound

This paper cites Modular meta-learning in abstract graph networks for combinatorial generalization.

Skill Expansion and Composition in Parameter Space Modular meta-learning in abstract graph networks for combinatorial generalization

Reference 2019

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Observation 376b62a5-f001-442f-8be6-d3028b2eb648 · outbound

This paper cites Data-Incremental Continual Offline Reinforcement Learning.

Skill Expansion and Composition in Parameter Space Data-Incremental Continual Offline Reinforcement Learning

Reference 2020

Resolution: verified exact; local_arxiv, observed 2026-08-08T17:26:09.968412Z

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Observation 401cbddc-5dff-4a2b-83b8-d0ab0978e4ef · outbound

This paper cites Continual Offline Reinforcement Learning via Diffusion-based Dual Generative Replay.

Skill Expansion and Composition in Parameter Space Continual Offline Reinforcement Learning via Diffusion-based Dual Generative Replay

Reference 2021

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Observation 7b3463b7-b000-46d6-8908-5ec41306d2b8 · outbound

This paper cites LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition.

Skill Expansion and Composition in Parameter Space LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition

Reference 2022

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Observation e2816dd5-8493-4ca9-b21d-c6397ae21122 · outbound

This paper cites Scaling offline model-based rl via jointly-optimized world-action model pretrain- ing.

Skill Expansion and Composition in Parameter Space Scaling offline model-based rl via jointly-optimized world-action model pretrain- ing

Reference 2023

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Observation ae54135e-6f6d-41fb-bfd3-f61a7c9e53c9 · outbound

This paper cites Solving Rubik's Cube with a Robot Hand.

Skill Expansion and Composition in Parameter Space Solving Rubik's Cube with a Robot Hand

Reference 2024

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Observation ea8eef2f-1577-4dfc-a3d2-f917ed0d8973 · outbound

This paper cites Multi-LoRA Composition for Image Generation.

Skill Expansion and Composition in Parameter Space Multi-LoRA Composition for Image Generation

Reference 2025

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