arXiv AI
Forty Shades of Blue: Quality-Diversity Alignment via Mode-Conditioned Reinforcement Learning
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Aug 10
CreativeInstruct: Scalably Teaching LLMs to Balance Quality, Creativity, and Diversity
arXiv:2608. 07460v1 Announce Type: cross Abstract: While post-training improves the capabilities of large language models (LLMs), it generally lowers their output diversity and creativity, negatively impacting tasks that explicitly require creativity (e.
arXiv Computation and Language
Oct 16
Training Reasoning Models to Generate Diverse Scientific Ideas
The paper introduces a method for training large language models to generate diverse, high‑quality scientific ideas grounded in input documents. By using a set‑level reinforcement learning objective that rewards ideas for being distinct from earlier ones, the authors train an 8B model that outperforms Claude Opus 4.8 and GPT‑6 Astra on a contamination‑free benchmark. Human experts rate the model’s idea sets as equally promising while containing slightly more unique ideas, and automated experiments show comparable executability.
arXiv Machine Learning
Aug 28
Learning Generalizable Behaviors for Terminal Agents
The paper introduces the Agentic Compositional Generalization hypothesis, suggesting that reinforcement learning (RL) primarily refines high‑level decision‑making behaviors that orchestrate pre‑trained low‑level skills, rather than teaching new domain‑specific skills from scratch. It proposes River, a training recipe that enhances reward quality by filtering low‑quality synthetic environments and adding process‑level behavior regularization. Using River, RL‑trained agents outperform other open‑source 8B models on four terminal‑agent benchmarks, achieving significant gains with fewer than 30% of the training environments.
arXiv Computation and Language
Oct 7
Improving Diversity in LLM Short Story Generation
arXiv:2610.06729v2 Announce Type: replace Abstract: Large language models (LLMs) can generate accurate responses, but these are void of diversity. We attempt to address this for the task of creative...
arXiv Computation and Language
Oct 5
HARPO: Hallucination-Aware Reinforcement Learning for Faithful and Creative Language Generation
HARPO is a reinforcement learning framework that jointly optimizes faithfulness and creativity in language generation. It uses a Hallucination-Aware Generative Reward Model (HA‑GRM) to evaluate both faithfulness and writing quality, and a Selective Activation Mechanism (SAM) that applies writing rewards only to hallucination‑free outputs. Experiments on Qwen models show that HARPO improves faithfulness scores and reduces hallucination rates while boosting creative‑writing performance.
arXiv AI
Oct 16
EliteReplay: Preserving Diverse Solutions in RL Post-Training via Quality-Diversity Search
EliteReplay is a post‑training method that combines GRPO with a quality‑diversity archive to preserve diverse solutions in reinforcement learning. By collecting distinct strategies from a frozen base model before training and replaying them during training, the approach improves accuracy while maintaining a broader set of correct solutions. On text‑to‑SQL tasks, EliteReplay outperforms baselines, achieving higher pass@128 scores and up to 77% more distinct correct strategies, and it better handles ambiguous questions by covering multiple interpretations.