RI

#1Reinforcement Learning Enhanced LLM Agents for Complex Vehicle Routing Problems

2026-09-01 | National University of Singapore, Sun Yat-sen University |2609.00859

This paper proposes RLEA, a multi-agent framework that uses a lightweight neural planner trained via Soft Q-learning to orchestrate LLM actions (refinement, RAG and memory retrieval) for automatically generating solver-ready code for complex VRP variants. The results are backed by strong empirical evidence, achieving a 62.5% success rate on 48 VRP variants using OR-Tools, outperforming the state-of-the-art DRoC by 16.67% while reducing runtime errors. The key insight is that instead of using expensive LLM calls for step-by-step decision making, training a small language model with RL to act as a policy network for selecting agent actions significantly reduces latency and improves exploration. This is highly relevant for LLM evolutionary search and multi-agent optimization, as the architecture of an RL-trained planner combined with an evolving memory pool directly addresses sample efficiency and continuous learning in automated algorithm design.

#2AlgoWorlds: Benchmarking Tool Use for Global Optimization in Algorithmic Worlds

2026-08-29 | Tencent, University of Southern California |2608.29397

Xu et al. introduce AlgoWorlds, a benchmark of 240 environments across 10 combinatorial optimization families designed to evaluate whether LLM agents can use tools to gather hidden instance data and output globally optimal decisions. The results are backed by extensive evaluations across 7 frontier models, demonstrating that while models like Claude Opus 4.8 achieve high feasibility (over 96%), they fail to reach exact global optimality (only 38.6% success). The key insight is that the primary bottleneck for LLMs in optimization is not information acquisition or constraint satisfaction, but the joint reasoning required to integrate information into a globally optimal configuration.

#1RideSkill: A Hierarchical Algorithm for Generalized Ride Sharing with LLM-Driven Automatic Evolution

2026-09-02 | Huawei, The Hong Kong University of Science and Technology |2609.02250

This paper proposes RideSkill, a hierarchical ride-sharing dispatch algorithm where a skill repository, a skill combiner and a vehicle repositioner are all generated offline via LLM-assisted evolutionary search. The results are backed by strong empirical evidence on large-scale NYC data (up to 1500 vehicles), outperforming MARL and prior LLM-based heuristics by 11-40% in reward while drastically reducing detour times. The key insight is the integration of a self-check audit loop where the LLM evaluates empirical rollout metrics against its intended design, alongside a zero-shot combiner that probes unknown objective functions at runtime to dynamically mix frozen skills. This is highly relevant for LLM evolutionary search and multi-agent optimization, as the self-auditing mechanism and multi-task fitness normalization directly address sample efficiency and search signal quality in automated algorithm design.

#1hLLM: Single Pass Decoding for Generative Reranking

2026-09-01 | Meta Platforms, Inc. |2609.01807

HLLM replaces the sequential autoregressive decoding of generative rankers with a single forward pass, using a lightweight self-attention head and the Hungarian algorithm to decode the optimal permutation directly from the LLM's prefill hidden states. The results are strongly backed by empirical numbers, demonstrating a 64x speedup (down to 28ms latency) on a proprietary dataset and a 45x speedup on Amazon Beauty, while maintaining lossless ranking quality compared to the autoregressive teacher. The key insight is the architectural pattern of bypassing token-by-token generation for constrained outputs by training with a differentiable relaxation (Sinkhorn) and decoding with an exact combinatorial solver on the hidden states.