The paper investigates how to predict which offspring in a Quality‑Diversity (QD) archive will be retained over time, rather than just entering the archive. By analyzing signals available before reproduction, the authors find that lineage depth is the most reliable indicator of long‑term persistence, though its predictive power varies with population size. Despite this insight, biasing parent selection toward these signals does not improve overall search performance and can even lower the QD‑score.
arXiv:2609.10052v1 Announce Type: new Abstract: LLM agents for sequential decision tasks are often post-trained with trajectory-level outcome labels, but such labels provide little supervision for pr...
arXiv:2608. 13297v1 Announce Type: new Abstract: In evolutionary search, a weak child can be a valuable ancestor that makes high-fitness regions reachable.
The paper introduces RePPO, a method that adapts Proximal Policy Optimization to jointly optimize a compact population of diverse, high‑quality policies using a population‑dependent, dynamically shaped reward. RePPO achieves a strong quality‑diversity tradeoff on standard continuous‑control tasks and scales to larger policy and behavior spaces, maintaining quality while significantly increasing diversity in pixel‑based tasks. The authors also demonstrate RePPO’s applicability to fine‑tuning large language models, showing for the first time that quality‑diversity reinforcement learning can scale to this domain.
arXiv:2601. 12401v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning large-scale generative models, such as diffusion and flow models, to align with complex human preferences and user-specified tasks.
arXiv:2611.05929v1 Announce Type: cross Abstract: Off-policy deep reinforcement learning trains on random samples from a replay buffer of past experience. In physical robot settings, that buffer is d...
SwiftExplorer is a training‑free diffusion model alignment plugin that addresses two key issues in objective‑guided sampling: the loss of diversity due to strong directional bias and the inefficiency of constant guidance. It introduces an Inheritance‑Restart exploration mechanism to prevent early convergence and enhance the likelihood of high‑reward trajectories, while a Quality‑Efficiency arbitration mechanism removes incorrect signals and dynamically stops generation when optimal reward gain is achieved. Experiments show that SwiftExplorer improves preference, fidelity, diversity, and richness across multiple evaluation metrics.
arXiv:2501. 04426v2 Announce Type: replace-cross Abstract: Offline diversity maximization under imitation constraints can transform demonstration data into a set of distinct behavioral policies, improving robustness to distribution shift without additional environment interaction.
Efficient Diversity-based Experience Replay (EDER) is introduced to enhance learning efficiency in deep reinforcement learning. It uses a determinantal point process to model sample diversity, prioritizes replay based on this diversity, and incorporates Cholesky decomposition and rejection sampling to handle large state spaces. Experiments on MuJoCo, Atari, and Habitat show that EDER significantly improves learning efficiency and performance in high‑dimensional, realistic environments.
Evolutionary Ensemble Search (EES) is a framework that builds machine‑learning procedures through expert‑guided program evolution. A specialized council interprets task evidence and experimental results to generate structured search directions, which an orchestrator assigns to execution specialists and an evolutionary engine. The engine selects parents, diagnoses errors, and creates descendants via code mutation, pipeline edits, and crossover, with each child evaluated on its own validation evidence. Population archives preserve useful alternatives, and compatible predictions compete in a validation‑gated ensemble stage. Search adapts through parent‑relative operator credit, session memory, and lessons retrieved across runs. The system achieved medal‑threshold artifacts on 19 of 22 tasks (86.36 %) with 11 gold, five silver, and three bronze outcomes across diverse modalities.
arXiv:2509.15194v4 Announce Type: replace-cross Abstract: Large language models (LLMs) can improve through reinforcement learning with verifiable rewards (RLVR), but many settings lack reference answ...
arXiv:2607. 23408v1 Announce Type: new Abstract: Expensive black-box optimization is ubiquitous in science and engineering, where function evaluations are costly and the evaluation budget is limited.
arXiv:2604. 15414v2 Announce Type: replace-cross Abstract: Continual reinforcement learning must balance retention with adaptation, yet many methods still rely on \emph{single-model preservation}, committing to one evolving policy as the main reusable solution across tasks.