arXiv:2604. 21991v2 Announce Type: replace Abstract: Multi-task optimization is a powerful approach for solving a large number of tasks in parallel.
arXiv:2509. 16959v5 Announce Type: replace-cross Abstract: When different objectives conflict with each other in multi-task learning, gradients begin to interfere and slow convergence, thereby potentially reducing the final model's performance.
arXiv:2606. 16774v1 Announce Type: new Abstract: Equipping Large Language Model (LLM) agents with effective skills is crucial for solving complex tasks in real-world systems like OpenClaw.
arXiv:2603. 02462v2 Announce Type: replace-cross Abstract: A key challenge in developing unified neural solvers for combinatorial optimization (CO) is the efficient generalization of models from a given set of tasks to new tasks unseen during initial training.
arXiv:2606. 29082v1 Announce Type: cross Abstract: Would experience designing faster GPU kernels also help close in on a long-standing open mathematical conjecture?
arXiv:2509. 24256v2 Announce Type: replace-cross Abstract: The pretrain-transfer paradigm, which underpins the success of large language models (LLMs), has demonstrated the immense power of creating foundation models that learn generalizable representations from vast datasets.
arXiv:2606. 22589v2 Announce Type: replace Abstract: Ever since the advent of foundation models and the pre-training-finetuning paradigm, there have been numerous efforts to merge multiple task-specific experts into a single multi-task model.
The paper introduces TPGC, a dual-prior prompt initialization method for multi-task graph pre-training. It first uses a Task-Prior Injection Module to pre-train prompts on an auxiliary graph, then a Structure-Prior Injection Module to embed global structural context into layer-wise prompt vectors. Experiments on six node and graph classification benchmarks show that TPGC outperforms state‑of‑the‑art baselines in few‑shot settings while requiring fewer tunable parameters and less runtime.
arXiv:2606. 18627v1 Announce Type: new Abstract: Model merging has emerged as a training-free alternative to multi-task learning, aiming to combine multiple task-specific fine-tuned models into a single multi-task model.
The paper presents Iterative Sequential Transfer (IST), a method for few-shot multiobjective multitask optimization that addresses the bottleneck of aligning elite solution distributions across tasks. IST treats multitask optimization as a sequence of transfer problems, focusing evaluations on one target task per iteration and using a likelihood-informed prioritization to select the task most ready for knowledge integration. Experiments on benchmark and real-world problems demonstrate IST’s effectiveness under tight evaluation budgets.
The paper introduces Iterative Sequential Transfer (IST), a method for few-shot multiobjective multitask optimization that addresses the challenge of aligning elite solution distributions across tasks. IST treats multitask optimization as a sequence of transfer problems, focusing evaluations on one target task per iteration and using a likelihood-informed prioritization to select the task most ready for knowledge integration. Experiments on benchmark and real-world problems demonstrate the method’s effectiveness under tight evaluation budgets.
EmbodiedMind introduces a three-stage training paradigm for embodied foundation models that tackles inefficient sample use, task imbalance, and credit assignment in long-horizon planning. The stages—Rejection Sampling-based Fine‑Tuning, Iterative Rejection GRPO, and Trie‑GRPO—filter low‑informative data, balance task difficulty, and use action prefix trees for step‑level advantage estimation. This approach yields a state‑of‑the‑art average performance of 70.02% across 18 benchmarks, notably improving long‑horizon task planning accuracy.
arXiv:2607. 17760v1 Announce Type: cross Abstract: Inverse reinforcement learning (IRL) provides a powerful framework for learning from demonstrations.