Paper Citation Record · LEDGER

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance

As of 8 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2507.06615.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.06615 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:06:27.128981Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6956b067-8341-46e0-bcbd-64795b02225e · outbound

This paper cites Dynamic programming.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Dynamic programming

Reference 1

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Observation af101913-8a09-4c41-8e5b-c7de8da6b9f2 · outbound

This paper cites OpenAI Gym.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance OpenAI Gym

Reference 2

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Observation 34431820-cad1-4701-beac-8212532aa7fc · outbound

This paper cites Multitask learning.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Multitask learning

Reference 3

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Observation 0c35eeb8-bef0-40c6-9f1b-7770423608ce · outbound

This paper cites Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:27.734718Z

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Observation d248c66f-4a2b-44e9-91d2-24c7eca148c3 · outbound

This paper cites Multi-task reinforcement learning with task representation method.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Multi-task reinforcement learning with task representation method

Reference 5

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Observation 9de1f464-3e53-4755-8767-6d82f0270712 · outbound

This paper cites Soft Actor-Critic for Discrete Action Settings.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Soft Actor-Critic for Discrete Action Settings

Reference 6

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Observation ead5de88-ef60-4ba9-b730-12769cb6e820 · outbound

This paper cites Divide- and-conquer reinforcement learning.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Divide- and-conquer reinforcement learning

Reference 7

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Observation 6ef8dc4f-3342-4c2c-86c4-449f807a9ccb · outbound

This paper cites Reinforcement learning with deep energy-based policies.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Reinforcement learning with deep energy-based policies

Reference 8

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Observation 16bb38e4-780d-4e03-91d3-dec7f354261c · outbound

This paper cites Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 9

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Observation 0ebc75cf-1afd-4094-845d-af7ef6f61713 · outbound

This paper cites Not all tasks are equally difficult: Multi-task deep reinforcement learning with dynamic depth routing.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Not all tasks are equally difficult: Multi-task deep reinforcement learning with dynamic depth routing

Reference 10

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Observation 4eb6ac94-69b8-4c8a-abea-48aed0d715bf · outbound

This paper cites Benchmark environments for multitask learning in continuous domains.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Benchmark environments for multitask learning in continuous domains

Reference 11

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Observation a8939339-cf84-4fdc-9900-350b4174c8ef · outbound

This paper cites End-to-end training of deep visuomotor policies.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance End-to-end training of deep visuomotor policies

Reference 12

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Observation 2176921b-bc88-4841-b2a8-0ba171809721 · outbound

This paper cites Lillicrap, Jonathan J.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Lillicrap, Jonathan J

Reference 13

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Observation 87174cbe-8b2a-4034-bce1-3a1152b3dbc6 · outbound

This paper cites Conflict-averse gradient descent for multi-task learning.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Conflict-averse gradient descent for multi-task learning

Reference 14

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Observation 2b49c235-0949-4445-82fb-b8b63b001962 · outbound

This paper cites Q- functionals for value-based continuous control.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Q- functionals for value-based continuous control

Reference 15

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Observation 1db05804-1920-4860-8fee-fadb7139911b · outbound

This paper cites Rusu, Joel Veness, Marc G.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Rusu, Joel Veness, Marc G

Reference 16

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Observation 4f258522-cbea-488b-970d-d9e46d6f86fa · outbound

This paper cites Overcoming exploration in reinforcement learning with demonstrations.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Overcoming exploration in reinforcement learning with demonstrations

Reference 17

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Observation 163539aa-9b26-4d44-94bf-750e50195f4e · outbound

This paper cites Markov Decision Processes: Discrete Stochastic Dynamic Programming.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Markov Decision Processes: Discrete Stochastic Dynamic Programming

Reference 18

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Observation 2194e526-57fb-41da-a89e-54786c8fc73d · outbound

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

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance An Overview of Multi-Task Learning in Deep Neural Networks

Reference 19

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This paper cites Hierarchical and interpretable skill acqui- sition in multi-task reinforcement learning.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Hierarchical and interpretable skill acqui- sition in multi-task reinforcement learning

Reference 20

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Observation f460c02b-9ff7-401b-b39c-8a451cc4b64f · outbound

This paper cites Multi-task reinforcement learning with context- based representations.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Multi-task reinforcement learning with context- based representations

Reference 21

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Observation 8f79f070-122f-407e-b8fc-4b0d5f47fbe5 · outbound

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

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance PaCo: Parameter- compositional multi-task reinforcement learning

Reference 22

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Observation 2b9f67cb-074c-445a-83ca-e899be6f3b62 · outbound

This paper cites Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, and Razvan Pascanu.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, and Razvan Pascanu

Reference 23

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Observation 158b1812-c2ad-4633-84a0-8b8e28132152 · outbound

This paper cites MuJoCo: A physics engine for model-based control.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance MuJoCo: A physics engine for model-based control

Reference 24

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Observation efcaf77a-b41a-4b39-b64b-899e88b3409e · outbound

This paper cites A survey of multi-task deep reinforcement learning.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance A survey of multi-task deep reinforcement learning

Reference 25

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Source-reported events for the cited work

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Observation 2f491275-5314-4ac8-ae9a-aca732c37924 · outbound

This paper cites Disentan- gling transfer in continual reinforcement learning.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Disentan- gling transfer in continual reinforcement learning

Reference 26

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Source-reported events for the cited work

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Observation 8f63fe28-5d59-4ced-b086-b51e99ab9290 · outbound

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

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Multi-task reinforcement learning with soft modularization

Reference 27

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Source-reported events for the cited work

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Observation e64095ba-b40e-49bf-82fa-fdc5b6a898cc · outbound

This paper cites Mastering complex control in MOBA games with deep reinforcement learning.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Mastering complex control in MOBA games with deep reinforcement learning

Reference 28

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Source-reported events for the cited work

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Observation 42273352-30d9-4d66-b59b-9720cee13b2d · outbound

This paper cites Conservative data sharing for multi-task offline reinforcement learning.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Conservative data sharing for multi-task offline reinforcement learning

Reference 29

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Observation 6980ee7d-36ad-44b6-8cc9-56000f31aaa3 · outbound

This paper cites Gradient surgery for multi-task learning.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Gradient surgery for multi-task learning

Reference 30

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Observation f910f7ae-4f18-40d3-a21a-d8be669fb90d · outbound

This paper cites Meta-World: A benchmark and evaluation for multi-task and meta reinforcement learning.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Meta-World: A benchmark and evaluation for multi-task and meta reinforcement learning

Reference 31

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Observation ac343e8c-760f-49a8-ba43-6f5424df160a · outbound

This paper cites QMP: Q-switch Mixture of Policies for Multi-Task Behavior Sharing.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance QMP: Q-switch Mixture of Policies for Multi-Task Behavior Sharing

Reference 32

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Observation 471e7da7-456e-48e7-941e-bb1777e50947 · outbound

This paper cites CUP: Critic-guided policy reuse.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance CUP: Critic-guided policy reuse

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:27.282117Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 25376279-8414-45bf-8121-e2fe020ec629 · outbound

This paper cites t+K−1X t′=t γt′−t (Ri(st′, at′) +αiH(πi(·|st′))) # + γKEst+K∼Pi h ˆQ˜g i (st+K, i) i = Eat′ ∼πi,st′+1∼Pi.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance t+K−1X t′=t γt′−t (Ri(st′, at′) +αiH(πi(·|st′))) # + γKEst+K∼Pi h ˆQ˜g i (st+K, i) i = Eat′ ∼πi,st′+1∼Pi

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:27.265399Z

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Observation ef72a0e4-770a-45bb-ab68-b30d2601b72d · outbound

This paper cites Under the initial episode length setting of 150 timesteps, 42 tasks achieve a success rate exceeding 90%.

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance Under the initial episode length setting of 150 timesteps, 42 tasks achieve a success rate exceeding 90%

Reference 150

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:27.250052Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

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