Discovering Reinforcement Learning Interfaces with Large Language Models

Discovering Reinforcement Learning Interfaces with Large Language Models | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/discovering-reinforcement-learning-interfaces-with-large-language-models

stale

Proof freshness: stale
Proof status: unverified
Display score: 9/10
Last proof check: 2026-05-06
Score updated: 2026-05-06
Score fresh until: 2026-06-05
References: 0
Source count: 4
Coverage: 67%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID discovering-reinforcement-learning-interfaces-with-large-language-models | Route /signal-canvas/discovering-reinforcement-learning-interfaces-with-large-language-models

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source_context

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Evidence Receipt

Route status: building

Claims: 12

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Discovering Reinforcement Learning Interfaces with Large Language Models

PDF: https://arxiv.org/pdf/2605.03408v1

Repository: https://github.com/Lossfunk/LIMEN

Source count: 4

Coverage: 67%

Last proof check: 2026-05-06T20:22:35.704Z

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Ready for execution: Discovering Reinforcement Learning Interfaces with Large Language Models

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Subject: Discovering Reinforcement Learning Interfaces with Large Language Models

Verdict

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Verdict is Build Now because viability and implementation proof cleared the Wave 1 scaffold thresholds.

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GitHub Code Pulse

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Last commit

5/11/2026

Forks

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Claim map

Strong 12Mixed 0Weak 0

Evidencepartial
We propose LIMEN, a LLM guided evolutionary framework that produces candidate interfaces as executable programs and iteratively refines them using policy training feedback.
Implicationpartial
Directly stated in the abstract with a clear description of the method.
Verificationpartial
partial
Evidencepartial
Across novel discrete gridworld tasks and continuous control domains spanning locomotion and manipulation, joint evolution of observations and rewards discovers effective interfaces given only a trajectory-level success metric.
Implicationpartial
Directly stated in the abstract as a key result.
Verificationpartial
partial
Evidencepartial
while optimizing either component alone fails on at least one domain.
Implicationpartial
Directly stated in the abstract as a finding.
Verificationpartial
partial
Evidencepartial
We study RL task interface discovery from raw simulator state, where both observation mappings and reward functions must be generated.
Implicationpartial
Directly stated in the abstract as the problem studied.
Verificationpartial
partial
Evidencepartial
We propose LIMEN, a LLM guided evolutionary framework that produces candidate interfaces as executable programs and iteratively refines them using policy training feedback.
Implicationpartial
Directly stated in the abstract with a clear description of the method.
Verificationpartial
partial
Evidencepartial
Across novel discrete gridworld tasks and continuous control domains spanning locomotion and manipulation, joint evolution of observations and rewards discovers effective interfaces given only a trajectory-level success metric.
Implicationpartial
Explicitly stated in the abstract as a key result.
Verificationpartial
partial
Evidencepartial
optimizing either component alone fails on at least one domain.
Implicationpartial
Directly stated in the abstract as a finding.
Verificationpartial
partial
Evidencepartial
We propose LIMEN, a LLM guided evolutionary framework that produces candidate interfaces as executable programs and iteratively refines them using policy training feedback.
Implicationpartial
Directly stated in the abstract with method description.
Verificationpartial
partial
Evidencepartial
joint evolution of observations and rewards discovers effective interfaces given only a trajectory-level success metric
Implicationpartial
Directly stated in abstract as a key result.
Verificationpartial
partial
Evidencepartial
single-component optimization fails catastrophically on at least one domain in our evaluation suite
Implicationpartial
Directly stated in abstract with emphasis on catastrophic failure.
Verificationpartial
partial
Evidencepartial
Across novel discrete gridworld tasks and continuous control domains spanning locomotion and manipulation
Implicationpartial
Explicitly listed in abstract.
Verificationpartial
partial
Evidencepartial
automatic construction of RL interfaces from raw state can substantially reduce manual engineering
Implicationpartial
Stated as a conclusion in abstract, supported by results.
Verificationpartial
partial

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Discovering Reinforcement Learning Interfaces with Large Language Models

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