arXiv Machine Learning
23h ago
Agentic ESOpt: Fine-Tuning Long-Horizon LLM Agents with Minimal GPU Requirements
arXiv:2608. 17310v1 Announce Type: new Abstract: Reinforcement Learning (RL) has been promising in single-turn LLM fine-tuning.
Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning
arXiv:2509. 24372v3 Announce Type: replace-cross Abstract: Fine-tuning large language models (LLMs) for downstream tasks is an essential stage of modern AI deployment.
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arXiv AI
5d ago
Beyond the Best Guess: Improving LLM Solution Coverage with Evolution Strategies
arXiv:2608. 12679v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in discovery domains such as math and science.
arXiv Machine Learning
Aug 4
Cooperative Coevolution for Resource-Constrained Agentic LLM Post-Training
arXiv:2608. 02391v1 Announce Type: cross Abstract: Tool-using large language model (LLM) agents produce long, multi-turn trajectories, making gradient-based post-training memory-intensive.
arXiv AI
Jul 7
A Technical Survey of Reinforcement Learning Techniques for Large Language Models
arXiv:2507. 04136v2 Announce Type: replace Abstract: This survey offers a comprehensive foundation on the integration of RL with language models, highlighting prominent algorithms such as Proximal Policy Optimization (PPO), Q-Learning, and Actor-Critic methods.
arXiv Machine Learning
1d ago
Le Critique: Privileged Value Functions for LLM Reinforcement Learning
arXiv:2608. 16739v1 Announce Type: new Abstract: Reinforcement learning algorithms for Large Language Models (LLMs) are largely distinguished by their variance reduction strategy.
arXiv Machine Learning
Aug 11
Matching Accuracy, Different Geometry: Evolution Strategies vs GRPO in LLM Post-Training
arXiv:2604. 01499v2 Announce Type: replace Abstract: Evolution Strategies (ES) have emerged as a scalable gradient-free alternative to reinforcement learning based LLM fine-tuning, but it remains unclear whether comparable task performance implies comparable solutions in parameter space.
arXiv AI
Jun 15
Quantized Evolution Strategies: High-precision Fine-tuning of Quantized LLMs at Low-precision Cost
arXiv:2602. 03120v2 Announce Type: replace-cross Abstract: Post-Training Quantization (PTQ) is essential for deploying Large Language Models (LLMs) on memory-constrained devices, yet it renders models static and difficult to fine-tune.
arXiv Machine Learning
Jun 2
Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning
arXiv:2603. 11653v2 Announce Type: replace Abstract: Continual Reinforcement Learning (CRL) for Vision-Language-Action (VLA) models is a promising direction toward self-improving embodied agents that can adapt in openended, evolving environments.
arXiv Machine Learning
Jul 9
UP: Unbounded Positive Asymmetric Optimization for Breaking the Exploration-Stability Dilemma
arXiv:2607. 06987v1 Announce Type: new Abstract: Reinforcement learning (RL) has become the standard paradigm for enhancing the complex reasoning capabilities of large language models (LLMs).
arXiv AI
Jun 3
EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning
arXiv:2606. 03108v1 Announce Type: new Abstract: Autonomous LLM training is often framed as recipe search, which leaves the training harness largely static.
arXiv AI
Jun 15
Rethinking the Trust Region in LLM Reinforcement Learning
arXiv:2602. 04879v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a cornerstone for fine-tuning Large Language Models (LLMs), with Proximal Policy Optimization (PPO) serving as the de facto standard algorithm.
arXiv AI
Jul 14
ARMOR: Stabilizing On-Policy LLM RL with Off-Policy Anchor Samples
arXiv:2607. 10481v1 Announce Type: cross Abstract: Reinforcement learning (RL) has significantly enhanced the reasoning capabilities of large language models (LLMs), yet the training process remains notoriously fragile.