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Cooperative Multi-Agent Reinforcement Learning via Epigraph-Form Guided Exploration

arXiv:2611.07066v1 Announce Type: new Abstract: Discovering cooperative behavior in multi-agent reinforcement learning (MARL) is challenging due to the combinatorial complexity of joint state-action spaces, which hinders the emergence of coordinated behaviors from trial-and-error alone. Intrinsic rewards are often used to aid discovery, but naively combining them with team objectives can distort the learning signal, compromising task performance. In this paper, we propose EFXPLORER, a constrained exploration framework that maximizes exploration objectives subject to a constraint that preserves established task performance. We solve the resulting problem using an epigraph reformulation that introduces adaptive exploration budgets. This approach separates intrinsic rewards from task objectives and regulates exploration through task feasibility. To further encourage diverse and temporally extended exploration, we incorporate a successor distance-based intrinsic reward that captures long-horizon dependencies. Empirically, our method outperforms strong baselines and induces novel cooperative strategies across SMAX, VMAS, and MPE benchmark suites.

Liquid AI unveils the LFM3 multimodal model family, emphasizing efficient, specialized intelligence

Recently, Liquid AI officially launched its next-generation foundation model family, LFM3. The first release includes six models with approximately 2 billion parameters, covering text, vision, audio, omni-modal, and decision-making capabilities—all built on the LFM3-1.7B-Base foundation and achieving a pre-training throughput of 22 trillion tokens. This series adopts a novel tri-hybrid layer architecture, integrating gated short convolutions, sliding windows, and other mechanisms, prioritizing fewer parameters and higher efficiency.

When to Stop Penalizing Actions? Martingale GRPO for VLA Reinforcement Learning

arXiv:2610.32431v1 Announce Type: new Abstract: A failed vision-language-action (VLA) rollout does not imply that every action contributed to failure. Yet in group-relative policy optimization (GRPO), a single trajectory-level advantage is assigned to all actions in a rollout. Potentially useful recovery attempts can thus be penalized alongside the errors they aim to correct. Ideal stopping conditions for failure feedback are characterized using success-probability and policy-score martingales. At these stops, the removed gradient contribution has zero conditional expectation given the retained information. Truncation therefore preserves the expectation of a reference GRPO group-gradient estimator and can reduce its covariance. One such stop occurs when the conditional probability of succe

POS-Signature Demonstration Retrieval for In-Context Dependency Parsing

arXiv:2610.31966v1 Announce Type: new Abstract: Demonstrations for in-context learning are often retrieved by semantic similarity, which may be a poor proxy for relevance in structured prediction. We study demonstration selection for Universal Dependencies parsing with a POS-signature policy that represents sentences with weighted part-of-speech statistics and retrieves three length-compatible, gold-parsed examples from the labeled training split. We evaluate two open-weight language models without fine-tuning on all 2,077 sentences of the UD English Web Treebank test set, comparing the policy with shot- and format-matched fixed and semantic three-shot baselines, as well as zero-shot, chain-of-thought, and critique-refinement systems. POS-signature retrieval yields the highest labeled atta

Researchers have reengineered a neural machine translation system for Sicilian, improving the quality of translations for low-resource languages.

A technical paper published on October 24, 2026, on arXiv (ID 2610.30374v1) reports the reconstruction of a small-scale neural machine translation (NMT) system for Sicilian: it leverages high-quality parallel corpora extracted from PDFs of the bilingual journal “Arba Sicula,” improves sentence alignment, tokenization, and subword segmentation that is sensitive to word stems, and migrates the original Perl‑dependent pipeline to a Perl‑free implementation. Experimental comparisons between a small Transformer trained from scratch and a fine-tuned pre-trained NLLB model demonstrate that the latter performs significantly better, particularly when translating into Sicilian.

Why it matters：This paper presents a reproducible, engineered NMT pipeline for the endangered/low-resource language Sicilian, covering key stages such as corpus construction, model adaptation, and optimization of training strategies. All evaluations are conducted on the frozen “Arba Sicula” test set, with transparent methods and clearly defined limitations, making it a valuable technical reference.

AQL-Lite Released: An Efficient Continuous Pre-training Framework for Modern Standard Arabic

On October 24, 2026, arXiv published paper 2610.30344v1, introducing the AQL-Lite framework, which significantly enhances the adaptability of multilingual large models to Modern Standard Arabic (MSA) through quality-first data filtering and continuous pre-training of selected parameters (CPT).

Why it matters：This study directly addresses key challenges in training large Arabic-language models—namely, the abundance of noisy data, dialectal diversity, and the high costs associated with continuous pre-training—by proposing a reproducible, lightweight, and cost-effective technical approach, which has been validated on multiple authoritative Arabic-language benchmarks. All conclusions are based on the experimental setup and results explicitly reported in the paper, without any external extrapolation.

Amazon SageMaker AI has launched a Multi-Step Reinforcement Learning (MTRL) framework, supporting end-to-end training of agents to accomplish multi-step tasks.

AWS has launched the Multi-Turn Reinforcement Learning (MTRL) feature, enabling users to train large‑model agents on SageMaker AI using full task trajectories and final outcome feedback, without requiring step‑level annotations. A end-to-end demonstration is provided using Wordle as an example, covering AgentCore deployment, data preparation, GRPO training, reward design, and cost analysis.

GPT-OSS-20B Multilingual Reasoning Fine-Tuning Experiment: SFT Enables Zero-Base Behavior Acquisition, while GRPO Only Effectively Improves Performance When a Probabilistic Headspace Exists

The AWS team conducted multilingual inference fine-tuning experiments on Amazon SageMaker HyperPod using GPT‑OSS‑20B, employing LoRA for parameter-efficient fine-tuning. SFT boosted the correctness rate of inference in five languages (English, French, German, Spanish, and Italian) from 22% to 100%, though the correctness rate for the answer language remained at only 62%. GRPO further improved the answer-language correctness rate to 70% without compromising inference-language performance; however, the gains varied across languages—for example, Spanish saw a +30 percentage-point increase, while Italian experienced a -20 percentage-point decline. The experiments clearly demonstrated that GRPO cannot bootstrap new capabilities from scratch and can only optimize behaviors that already occur sporadically.