Evolutionary Discovery of Reinforcement Learning Algorithms via Large Language Models

Evolutionary Discovery of Reinforcement Learning Algorithms via Large Language Models | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/evolutionary-discovery-of-reinforcement-learning-algorithms-via-large-language-models

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-03-31
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 44
Source count: 3
Coverage: 50%

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 evolutionary-discovery-of-reinforcement-learning-algorithms-via-large-language-models | Route /signal-canvas/evolutionary-discovery-of-reinforcement-learning-algorithms-via-large-language-models

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Evidence Receipt

Route status: building

Claims: 8

References: 44

Proof: Verification pending

Freshness state: computing

Source paper: Evolutionary Discovery of Reinforcement Learning Algorithms via Large Language Models

PDF: https://arxiv.org/pdf/2603.28416v1

Source count: 3

Coverage: 50%

Last proof check: 2026-03-31T20:17:39.447Z

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Watch and verify: Evolutionary Discovery of Reinforcement Learning Algorithms via Large Language Models

/buildability/evolutionary-discovery-of-reinforcement-learning-algorithms-via-large-language-models

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Subject: Evolutionary Discovery of Reinforcement Learning Algorithms via Large Language Models

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Preparing verified analysis

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Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
We present an evolutionary framework for discovering reinforcement learning algorithms by searching directly over executable update rules that implement complete training procedures.
Implicationpartial
Directly stated in the abstract and repeated in the parsed sections with clear methodology description.
Verificationpartial
partial
Evidencepartial
To promote the emergence of nonstandard learning rules, the search excludes canonical mechanisms such as actor–critic structures, temporal-difference losses, and value bootstrapping.
Implicationpartial
Explicitly stated in the abstract and parsed sections with clear exclusion criteria.
Verificationpartial
partial
Evidencepartial
Because reinforcement learning algorithms are highly sensitive to internal scalar parameters, we introduce a post-evolution refinement stage in which a large language model proposes feasible hyperparameter ranges for each evolved update rule.
Implicationpartial
Directly stated in abstract and detailed in parsed sections with specific implementation details.
Verificationpartial
partial
Evidencepartial
Evaluated end-to-end by full training runs on multiple Gymnasium benchmarks, the discovered algorithms achieve competitive performance relative to established baselines, including SAC, PPO, DQN, and A2C.
Implicationpartial
Explicitly stated in abstract with supporting evaluation protocol described in parsed sections.
Verificationpartial
partial
Evidencepartial
As shown in Figure 5, enforcing structural similarity improves both convergence speed and final fitness relative to unconstrained mutation, indicating that similarity-aware variation stabilizes search in update-rule space.
Implicationpartial
Supported by ablation study results showing improved convergence and fitness with regularization.
Verificationpartial
partial
Evidencepartial
The framework is computationally expensive, since each candidate update rule must be evaluated through full reinforcement-learning training across multiple environments and random seeds, which restricts the scale of evolutionary search.
Implicationpartial
Explicitly stated as a limitation in the parsed sections with clear explanation.
Verificationpartial
partial
Evidencepartial
In the current setting, the discovered algorithms arise from novel recombinations of existing reinforcement-learning mechanisms rather than from fundamentally new update forms.
Implicationpartial
Directly stated in limitations section, though requires some interpretation of what constitutes 'fundamentally new'.
Verificationpartial
partial
Evidencepartial
The evolutionary fitness is defined as the mean normalized performance across environments, F(f)= 1/N ∑_{i=1}^N F̃_i(f).
Implicationpartial
Explicitly defined with mathematical formulation in parsed sections.
Verificationpartial
partial

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Evolutionary Discovery of Reinforcement Learning Algorithms via Large Language Models

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