arXiv AI
Jun 2

MindGames Arena Generalization Track: In2AI Solution with Delayed Per-Step Reward Attribution

arXiv:2606. 00017v1 Announce Type: new Abstract: Training language model agents for multi-agent strategic interaction presents a core difficulty: the quality of any action may depend on future events that never materialize, on moves that violate game rules, or on decisions made by other players.

By Aliaksei Korshuk, Alexander Buyantuev, Ilya Makarov
arXiv AI
Jul 7

Regime-Conditional Stabilisation of LLM-Augmented Cooperative Multi-Agent Reinforcement Learning

arXiv:2607. 04470v1 Announce Type: cross Abstract: Large Language Models (LLMs) offer a natural interface for translating human objectives into reward signals for cooperative multi-agent reinforcement learning (MARL), yet the training-time dynamics of this integration remain poorly understood.

By Faid Keddouri, Sohaib Houhou, Aissa Boulmerka, Nadir Farhi
arXiv AI
Oct 19

Reward Engine: Reward-Centric Self-Evolution for Open-Ended Tool Agents

Reward Engine is a method for creating a reward function for open‑ended tool agents by first determining what success looks like and then optimizing toward it. It builds a RewardPack of test cases and rubrics from initial trajectories, which is frozen and used to guide the agent’s output selection and can also serve as a reinforcement‑learning reward. Experiments on WorkBuddy Bench Office and Claw‑Eval show that the constructed reward aligns with benchmark graders on most candidate pairs and improves three‑run consistency from 45.0% to 54.0% and from 78.4% to 83.7%.

By Litong Feng, Ji Qi, Yiran Cheng, Shaowu Pan, Xinyu Zhang, Qijun Li
arXiv AI
Sep 2

Explore More, Drift Less: Outcome-Only Reinforcement Learning Can Suffice for Long-Horizon Interactive Agents

The paper proposes CANOPY, a minimalist reinforcement learning protocol that addresses two common pitfalls—signal starvation and policy drift—in outcome‑only RL for long‑horizon interactive tasks. By scaling same‑task exploration, keeping updates on‑policy, and anchoring updates with KL divergence, CANOPY enables a Qwen3‑14B agent to achieve top leaderboard results on the AppWorld coding benchmark without auxiliary supervision or elaborate scaffolding. The approach also improves performance on SWE‑bench for a Qwen3.5‑9B model.

By Liming Pu, Xiaoxia Li, Yifu Liu, Teng Cao, Bin Yang