Paper Citation Record · LEDGER

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning

As of 15 August 2026, this Paper Citation Record lists 83 of 83 outbound references and 8 inbound Pith citation observations for arXiv:2509.24372.

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

pith.paper-citation-record.v1
2509.24372 v3

Coverage vector

measured 83 of 83 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T14:43:29.945895Z

measured 91 of 91 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T11:15:06.694233Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

83 of 83 outbound references displayed

verified exact7
verified fuzzy0
unresolved76
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation ffed6561-48be-4931-a9be-ae1b5c170ceb · outbound

This paper cites GPT-4 Technical Report.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:16.944824Z

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Observation 4a2b85df-7f2a-42e6-a1c6-84f78c69bb68 · outbound

This paper cites Intrinsic dimensionality explains the effectiveness of language model fine-tuning.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Intrinsic dimensionality explains the effectiveness of language model fine-tuning

Reference 2

Resolution: verified exact; doi, observed 2026-08-04T14:43:27.589291Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d92eb794-c4cf-461a-887c-578877622de4 · outbound

This paper cites Llama 3 model card, 2024.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Llama 3 model card, 2024

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:17.244748Z

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Observation 70bbafff-d02b-452a-989e-25dfb20a7668 · outbound

This paper cites Evolutionary optimization of model merging recipes.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Evolutionary optimization of model merging recipes

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:17.443331Z

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Observation 1cf56819-0c16-469d-a314-258c68a9f53b · outbound

This paper cites Introducing Claude 4, 2025.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Introducing Claude 4, 2025

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:17.695291Z

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Observation a775b486-7838-497b-bbfe-946235b2934d · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:17.934749Z

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Observation debbf38d-33ca-4e10-aaa2-9c6cfd9ecb91 · outbound

This paper cites Understanding pre-training and fine-tuning from loss landscape perspectives.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Understanding pre-training and fine-tuning from loss landscape perspectives

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:18.060517Z

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Observation 40e0d99b-048c-4be0-af24-9cac8f899fea · outbound

This paper cites On the weaknesses of reinforcement learning for neural machine translation.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning On the weaknesses of reinforcement learning for neural machine translation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:18.304745Z

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Observation 1448cc4e-5477-4bc3-98e4-0d327e955730 · outbound

This paper cites Back to basics: benchmarking canonical evolution strategies for playing atari.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Back to basics: benchmarking canonical evolution strategies for playing atari

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:18.515288Z

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Observation ccda3c47-12d6-4009-a746-794a5dffbac2 · outbound

This paper cites Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:18.684764Z

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Observation b1cadf89-8871-4d75-9cd2-ec44aed1fb11 · outbound

This paper cites Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:18.815282Z

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Observation 116f2248-7d3d-4cfb-b0cd-7c9ff0d9f382 · outbound

This paper cites Knowledge fusion by evolving weights of language models.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Knowledge fusion by evolving weights of language models

Reference 12

Resolution: verified exact; doi, observed 2026-08-04T14:43:27.258406Z

Source-reported events for the cited work

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Observation b97881ce-5a75-415f-b8b7-f4646c3872b1 · outbound

This paper cites Detecting hallucinations in large language models using semantic entropy.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Detecting hallucinations in large language models using semantic entropy

Reference 13

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Observation 97dabea9-1298-426d-8912-3e57462f0dc7 · outbound

This paper cites Reward shaping to mitigate reward hacking in RLHF.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Reward shaping to mitigate reward hacking in RLHF

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:19.404754Z

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Observation ef754ffa-0924-4677-9409-758a7b7867d4 · outbound

This paper cites Cognitive behaviors that enable self-improving reasoners, or, four habits of highly effective ST ars.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Cognitive behaviors that enable self-improving reasoners, or, four habits of highly effective ST ars

Reference 15

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Observation 1cbe0367-b8b7-4903-a123-a226527c892b · outbound

This paper cites Scaling laws for reward model overoptimization.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Scaling laws for reward model overoptimization

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:19.857855Z

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Observation 289e40bd-8b2f-428e-afed-01bc37e65ae0 · outbound

This paper cites Deep learning, volume 1.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Deep learning, volume 1

Reference 17

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Observation 2178f773-8149-4329-991e-b487a03a513f · outbound

This paper cites Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities., 2025.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities., 2025

Reference 18

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Observation d62c2a83-2353-42b8-a766-2c584ced21d5 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 19

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Observation a7036ecf-177d-481a-a876-e664d29aaf9b · outbound

This paper cites Deepseek-r1 incentivizes reasoning in llms through reinforcement learning.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Deepseek-r1 incentivizes reasoning in llms through reinforcement learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:20.764759Z

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Observation ea0ae0a5-8475-4dfd-a79d-d2757b03ddaf · outbound

This paper cites Connecting large language models with evolutionary algorithms yields powerful prompt optimizers.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Connecting large language models with evolutionary algorithms yields powerful prompt optimizers

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:21.051034Z

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Observation 1dba326f-0240-4c76-9f03-f8b5a2b7d07f · outbound

This paper cites Completely derandomized self-adaptation in evolution strategies.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Completely derandomized self-adaptation in evolution strategies

Reference 22

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Observation 18b5cfba-bd1e-4db9-b35f-19b29e9e4410 · outbound

This paper cites When evolution strategy meets language models tuning.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning When evolution strategy meets language models tuning

Reference 23

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Observation 9e173aea-9f86-4c5d-9ce1-329aff70b79b · outbound

This paper cites Neuroevolution for reinforcement learning using evolution strategies.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Neuroevolution for reinforcement learning using evolution strategies

Reference 24

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Observation 66a764d1-4096-4b16-ac11-46cd21524b4a · outbound

This paper cites Do we need to verify step by step? rethinking process supervision from a theoretical perspective.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Do we need to verify step by step? rethinking process supervision from a theoretical perspective

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:21.904752Z

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Observation ec6ea8c0-8122-4c0d-ab22-977bbb2993b4 · outbound

This paper cites Mixtral of Experts.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Mixtral of Experts

Reference 26

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Observation b00a1de9-0509-45d4-888f-42b39fc7ed0e · outbound

This paper cites Derivative-free optimization for low-rank adaptation in large language models.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Derivative-free optimization for low-rank adaptation in large language models

Reference 27

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Observation 2bb53075-2a59-4fca-b33f-64d302e00a34 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Adam: A Method for Stochastic Optimization

Reference 28

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Observation ecbb5529-880d-4d57-98c6-d33f02e7a269 · outbound

This paper cites Fine-tuning chatgpt for automatic scoring.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Fine-tuning chatgpt for automatic scoring

Reference 29

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Observation 26c35849-849e-4738-9715-b2b353ed2dc9 · outbound

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Unresolved cited work

Reference 30

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Observation 955ec27f-6a47-4a9f-bf3d-64b382b1f973 · outbound

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Unresolved cited work

Reference 31

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Observation 3ee762dd-3f1a-4278-b0a8-5ac1b622d931 · outbound

This paper cites DeepSeek-V3 Technical Report.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning DeepSeek-V3 Technical Report

Reference 32

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Observation 71fa5a7d-e6e4-49a7-8ff6-bf5646c24d07 · outbound

This paper cites Sparse me ZO : Less parameters for better performance in zeroth-order LLM fine-tuning, 2025.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Sparse me ZO : Less parameters for better performance in zeroth-order LLM fine-tuning, 2025

Reference 33

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Observation efa07711-73e6-4449-971f-3fdcc02cff0b · outbound

This paper cites Utilizing Evolution Strategies to Train Transformers in Reinforcement Learning.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Utilizing Evolution Strategies to Train Transformers in Reinforcement Learning

Reference 34

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Fine-tuning language models with just forward passes

Reference 35

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Observation a1cfbdc1-a4a6-490a-95fc-77c111fe4117 · outbound

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Nelson, Herbie Bradley, Adam Gaier, Arash Moradi, Amy K

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:23.664754Z

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Observation 9788c601-c4b5-4d58-b29e-254452e3d465 · outbound

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning What is artificial superintelligence?, 2023

Reference 37

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning AlphaEvolve: A coding agent for scientific and algorithmic discovery

Reference 38

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Unresolved cited work

Reference 39

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Tinyzero

Reference 40

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This paper cites Chen, Xi Chen, Tamim Asfour, Pieter Abbeel, and Marcin Andrychowicz.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Chen, Xi Chen, Tamim Asfour, Pieter Abbeel, and Marcin Andrychowicz

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:24.624754Z

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Observation 49a2d506-5899-4534-a9bc-98b445aefd8f · outbound

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Semantic density: Uncertainty quantification for large language models through confidence measurement in semantic space

Reference 42

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Observation 8e7782ce-fd85-4227-b42a-3ca5eb9fa557 · outbound

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Manning, and Chelsea Finn

Reference 43

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Rechenberg

Reference 44

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Unresolved cited work

Reference 45

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Pawan Kumar, Emilien Dupont, Francisco J

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:25.494752Z

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Code Llama: Open Foundation Models for Code

Reference 47

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning u ckstie , Martin Felder, and J \

Reference 48

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning u ckstie , Frank Sehnke, Tom Schaul, Daan Wierstra, Yi Sun, and J \

Reference 49

Resolution: verified exact; doi, observed 2026-08-04T14:49:22.811963Z

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Improved techniques for training gans

Reference 50

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Evolution Strategies as a Scalable Alternative to Reinforcement Learning

Reference 51

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This paper cites How well can a genetic algorithm fine-tune transformer encoders? a first approach.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning How well can a genetic algorithm fine-tune transformer encoders? a first approach

Reference 52

Resolution: verified exact; doi, observed 2026-08-04T14:49:22.554438Z

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Approximating kl divergence, 2020

Reference 53

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:26.444749Z

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This paper cites Numerische Optimierung von Computermodellen mittels der Evo-lutionsstrategie, volume 26.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Numerische Optimierung von Computermodellen mittels der Evo-lutionsstrategie, volume 26

Reference 55

Resolution: verified exact; doi, observed 2026-08-04T14:49:22.266846Z

Source-reported events for the cited work

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Observation 021a19c9-2ada-4bc5-ad18-5b943bbf7621 · outbound

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Parameter-exploring policy gradients

Reference 56

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Observation 5299c176-c970-41f7-96bf-b9b272b65ff1 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:26.852975Z

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Towards Expert-Level Medical Question Answering with Large Language Models

Reference 58

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning PRMB ench: A fine-grained and challenging benchmark for process-level reward models

Reference 59

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Unresolved cited work

Reference 60

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning A Technical Survey of Reinforcement Learning Techniques for Large Language Models

Reference 61

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

Reference 62

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning BBT v2: Towards a gradient-free future with large language models

Reference 63

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Observation b60689ed-5695-4a7d-9fbc-0ae90570da75 · outbound

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Black-box tuning for language-model-as-a-service

Reference 64

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This paper cites Sutton and Andrew G.

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Sutton and Andrew G

Reference 65

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Fine-tuning mt5-based transformer via cma-es for sentiment analysis

Reference 66

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 67

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Observation 8fd0b78e-4c31-44bd-9bf2-edcd19b001ad · outbound

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Solving math word problems with process- and outcome-based feedback

Reference 68

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Andrew Bagnell

Reference 69

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning When large language models meet evolutionary algorithms: Potential enhancements and challenges

Reference 70

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Natural evolution strategies

Reference 71

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Natural evolution strategies

Reference 72

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning BloombergGPT: A Large Language Model for Finance

Reference 73

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Evolutionary computation in the era of large language model: Survey and roadmap

Reference 74

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Qwen2.5-1M Technical Report

Reference 75

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning On the Relationship Between the OpenAI Evolution Strategy and Stochastic Gradient Descent

Reference 76

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning The Lessons of Developing Process Reward Models in Mathematical Reasoning

Reference 77

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Genetic prompt search via exploiting language model probabilities

Reference 78

Resolution: verified exact; doi, observed 2026-08-04T14:49:21.859507Z

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Observation cea816d8-261a-442c-b0f6-df389bed24ac · outbound

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning DPO meets PPO : Reinforced token optimization for RLHF

Reference 79

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning write newline

Reference 80

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning @esa (Ref

Reference 81

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Unresolved cited work

Reference 82

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Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning Unresolved cited work

Reference 83

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Pith citing papers

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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training cites this paper.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning

Reference 44

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ESSAM: A Novel Competitive Evolution Strategies Approach to Reinforcement Learning for Memory Efficient LLMs Fine-Tuning cites this paper.

ESSAM: A Novel Competitive Evolution Strategies Approach to Reinforcement Learning for Memory Efficient LLMs Fine-Tuning Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning

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Observation c079d1a8-4bcd-4cf5-b57b-8adc4393a860 · inbound

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Goal-Conditioned Supervised Learning for LLM Fine-Tuning Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning

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Observation 4dbd30cf-5d48-4e6f-9b4f-3bcbc479737f · inbound

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PopuLoRA: Co-Evolving LLM Populations for Reasoning Self-Play Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning

Reference 54

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Mathematical perspective on genetic algorithms with optimization guided operators Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning

Reference 40

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Why can genetic algorithms work in high-dimensional search spaces? Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-15T02:21:57.501664Z

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Observation 1fa9f782-49b7-4896-a4c0-e3fef6f00255 · inbound

Reward-Aware Population Scaling of Evolutionary Strategies in LLM Fine-Tuning cites this paper.

Reward-Aware Population Scaling of Evolutionary Strategies in LLM Fine-Tuning Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning

Reference 12

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Hyper-ES: Effective Evolution Strategies for LLM Reasoning via Descent Direction Merging Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning

Reference 12

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