Lev‑3B is a 3‑billion‑parameter decision model built on Granite‑4.1‑3B‑Base that uses a two‑stage training recipe. First, it learns bounded natural‑language decisions without log data, then it trains on logs while mixing target tasks with 35 unrelated auxiliary log tasks. The shared checkpoint performs better on unseen sources than training on logs alone, and outperforms prompted frontier models on anomaly detection at lower serving cost, though it lags on attack‑technique classification and shows calibration issues on excluded logging systems.
By Leo Betthauser, Yingqi Liu, Uday Shah, Jishang Wei, Sainyam Galhotra, Liang Gou, Vedant Dharnidharka, Hao Yang
The paper introduces LoRD, a lightweight post‑hoc calibration framework designed to improve confidence reliability in language‑model‑based log anomaly detectors. LoRD learns route‑specific reliability models from latent representations of correctly classified validation samples and uses reconstruction distances to estimate prediction reliability. By selectively recalibrating high‑risk predictions, LoRD reduces overconfident errors while maintaining strong anomaly detection performance across four large‑scale log benchmark datasets.
By Bin Li, Dongdong Wang, Siyang Lu
Reliable reinforcement learning (RL) agents must maintain operational integrity amidst sensor malfunctions, dynamic disturbances, and slow environmental shifts. The detection of out-of-distribution conditions is pivotal to determining when an agent's observations, transitions, or trajectory dynamics deviate from the assumptions underpinning its policy training.
arXiv:2607. 07976v1 Announce Type: cross Abstract: Reinforcement learning (RL) has achieved remarkable success in enhancing the reasoning capabilities of large language models (LLMs).
By Xiuyi Lou, Zicheng Xu, Yu-Neng Chuang, Hoang Anh Duy Le, Zhaozhuo Xu, Guanchu Wang, Vladimir Braverman
arXiv:2607. 08373v1 Announce Type: cross Abstract: Connected vehicles are autonomous cyber-physical systems whose behavior must be continuously monitored during operation to detect deviations from normal operation before they propagate into failures.
By Matthias Wei{\ss}, Athreya Hosahalli Prakash, Maurice Artelt, Falk Dettinger, Nasser Jazdi, Michael Weyrich
The paper introduces T1, a 122‑billion‑parameter Mixture‑of‑Experts model trained with reinforcement learning to perform long‑horizon terminal tasks such as coding and scientific discovery. T1 operates a real shell in a cloud sandbox, making over 300 tool‑call turns per task and receiving rewards from task‑specific verifiers. The authors detail a training recipe that includes aggressive warm‑starting, TITO construction with drift repair, and rollout‑routing replay, achieving significant performance gains on Terminal‑Bench 2.1 and surpassing GPT‑5.4 and GLM‑5.1 on the Long‑Horizon Terminal Bench.
By Junyao Yang, Yucheng Shi, Zhongzhi Li, Ruhan Wang, Zongxia Li, Haitao Mi, Leowei Liang
arXiv:2606. 05885v1 Announce Type: new Abstract: Long-horizon LLM agents require reinforcement learning methods that can assign credit to intermediate decisions under sparse and delayed rewards.
By Yuanfan Li, Qi Zhou, Wenjing Duan, Lu Chen
arXiv:2607. 13988v1 Announce Type: new Abstract: Multi-turn agents solve complex tasks through extended sequences of tool interactions before producing a final answer, making credit assignment a fundamental challenge during post-training.
By Leitian Tao, Baolin Peng, Wenlin Yao, Tao Ge, Hao Cheng, Mike Hang Wang, Jianfeng Gao, Sharon Li
arXiv:2607. 12523v1 Announce Type: cross Abstract: Reliable reinforcement learning (RL) agents must maintain operational integrity amidst sensor malfunctions, dynamic disturbances, and slow environmental shifts.
By Emil Mittag, Richard Dazeley, Peter Vamplew
arXiv:2608. 05102v1 Announce Type: new Abstract: Long-horizon search agents must make multiple sequential actions (steps) to search, retrieve, verify, and integrate evidence to reach a final answer.
By Yijun Lu, Rui Ye, Jiajun Wang, Yuwen Du, Tian Jin, Songhua Liu, Siheng Chen
The paper introduces Entropy‑Normalized Trust Region (ENTR), a method for asynchronous reinforcement learning that adjusts off‑policy ratio thresholds based on token entropy rather than a single magnitude cut‑off. By recognizing that the natural scale of the ratio is set by entropy, ENTR preserves genuine exploration while filtering out noise from low‑entropy, stale data. Experiments on long‑horizon agentic tasks and mathematical reasoning benchmarks show ENTR outperforms existing asynchronous methods, improving BrowseComp‑Plus performance by 6.9 % and enabling stable training up to 30 policy versions of staleness while matching synchronous GRPO at a 2.6× speedup.
By Guanqun Zhao, Zijun Xie, Binbin Zheng, Yehan Yang, Jiafeng Lu, Aoqi Hu, Enlei Gong, Zeyu Chen
arXiv:2607. 09641v1 Announce Type: cross Abstract: Financial anomaly detection suffers from extreme class imbalance, causing traditional single-objective algorithms to exhibit ``fraud collapse'', defaulting to the majority class and failing to balance anomaly interdiction with customer friction.
By Cl\'audio L\'ucio do Val Lopes, Lucca Machado da Silva