arXiv AI
Learning What to Activate: Combinatorial Capability Allocation for Long-Horizon Multimodal Agents
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arXiv AI
Aug 19
Task Specialization Fine-Tuning for Contextual Reinforcement Learning
The paper introduces Task Specialization Fine-Tuning (TSFT), an online framework that allocates a limited fine‑tuning budget across multiple task regions in Contextual Reinforcement Learning. TSFT predicts fine‑tuning performance with a simple parametric model and solves the budget allocation problem exactly using integer linear programming. Experiments on combinatorial optimization, continuous control, and LLM fine‑tuning show that TSFT outperforms baselines in task coverage and approaches oracle performance.
arXiv Machine Learning
Aug 4
Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning
arXiv:2608. 01743v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a central paradigm for large language model (LLM) post-training, but optimization toward new objectives can degrade capabilities already present in the base model.
arXiv AI
Sep 15
LIMBO: Lifelong Inference-Time Memory and Budget Optimization for LLM Agents
arXiv:2609.14138v1 Announce Type: cross Abstract: As LLM agents become integrated into increasingly complex workflows, they must continually acquire new capabilities while retaining competence on pre...
arXiv Computation and Language
Aug 21
SPyCE: Skill-Policy Co-evolution for Multimodal Agents
arXiv:2607. 13854v2 Announce Type: replace Abstract: Multimodal agents that think with images iteratively manipulate visual evidence and invoke tools across many steps.
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 Computer Vision
4d ago
CapField-OPD: Learning Continuous Capability Fields via Joint-Anchored Multi-Teacher On-Policy Distillation for Flow Models
arXiv:2609.34658v2 Announce Type: replace Abstract: Reward-specialized post-training produces strong experts for flow-based generative models, while multi-teacher on-policy distillation (OPD) consoli...