Large language models increasingly require structured inference, from enforcing JSON schema to multilingual parsing, where outputs must satisfy complex constraints. We introduce MetaJuLS, a meta-reinforcement learning approach that learns universal constraint propagation policies applicable across languages and tasks without task-specific retraining. By formulating structured inference as adaptive constraint propagation and training a Graph Attention Network with meta-learning, MetaJuLS achieves 1.5-2.0× speedups over GPU-optimized baselines while maintaining an accuracy within 0.2% of that of state-of-the-art parsers. On Universal Dependencies across 10 languages and LLM-constrained generation (LogicBench, GSM8K-Constrained), MetaJuLS demonstrates rapid cross-domain adaptation: a policy trained on English parsing adapts to new languages and tasks with 5–10 gradient steps (5–15 seconds) rather than requiring hours of task-specific training. Mechanistic analysis reveals that the policy employs human-like parsing strategies (easy-first) and novel, non-intuitive heuristics. By reducing the number of propagation steps in LLM deployments, MetaJuLS contributes to Green AI by directly reducing the inference carbon footprint.

Proxy optimization, where AI systems exploit evaluator weaknesses rather than improve intended objectives, threatens both reinforcement learning (reward hacking) and LLM alignment (evaluator gaming). We introduce the Evaluator Stress Test (EST), an invariance-based framework that detects proxy gaming by separating exploitable sensitivity (e.g., format, physics bugs) from content-driven improvements using controlled perturbations with semantic validity audits. We validate EST across both domains. In RL, across 15 environments and 5 algorithms (2,156 expert-annotated episodes), EST achieves 78.4% precision and 81.7% recall. In LLM alignment, across 4 tasks, 2 model scales, 2 training methods, and 2 judges (1,200 human-annotated instances), EST achieves 74.2% precision and 78.6% recall with early warning signals preceding quality decline. Cross-domain analysis reveals that proxy-true correlation tracking transfers directly between domains, while perturbation design requires domain adaptation. Closed-loop mitigation improves human win-rate by 8.3 points (LLM) and reduces hacking by 54.6% (RL). We release benchmarks for both domains: 2,156 RL episodes and 1,200 LLM instances.

Post-training activation compression is essential for deploying Large Language Models (LLMs) on resource-constrained hardware. However, standard methods like Singular Value Decomposition (SVD) are gradient-blind: they preserve high-variance dimensions regardless of their impact on factual knowledge preservation. We introduce Fisher-Aligned Subspace Compression (FASC), a knowledge-aware compression framework that selects subspaces by directly modeling activation-gradient coupling, minimizing a second-order surrogate of the loss function. FASC leverages the Fisher Information Matrix to identify dimensions critical for factual knowledge, which often reside in low-variance but high-gradient-sensitivity subspaces. We propose the Dependence Violation Score (ρ) as a general-purpose diagnostic metric that quantifies activation-gradient coupling, revealing where factual knowledge is stored within transformer architectures. Extensive experiments on Mistral-7B and Llama-3-8B demonstrate that FASC preserves 6–8% more accuracy on knowledge-intensive benchmarks (MMLU, LAMA) compared to variance-based methods at 50% rank reduction, effectively enabling a 7B model to match the factual recall of a 13B uncompressed model. Our analysis reveals that ρ serves as a fundamental signal of stored knowledge, with high-ρ layers emerging only when models internalize factual associations during training

As the deployment of AI models shifts towards edge devices, developing efficient sequence models has become critical. State-space models (SSMs), particularly Mamba, have emerged as strong rivals to Transformers due to their linear-time complexity and impressive performance across a range of tasks. However, their large parameter counts still hinder their use in resource-constrained environments. To address this, we propose a novel unstructured pruning framework specifically tailored for Mamba, achieving up to 70% parameter reduction with only a 3–9% drop in performance. Unlike pruning techniques designed for Transformers, our approach leverages Mamba’s unique recurrent dynamics by incorporating pruning based on both weight and gradient importance to preserve critical parameters, a gradual pruning schedule to maintain model stability, and a global strategy to optimize parameter allocation across the model. Extensive experiments on the WikiText-103, Long Range Arena, and ETT benchmarks demonstrate significant efficiency gains, including 1.77× faster inference and a 46% reduction in memory usage. Our component analysis confirms Mamba’s robustness to pruning, highlighting the framework’s potential for enabling practical deployment while underscoring the need for careful evaluation to avoid introducing biases in sensitive applications.

Integrating large language models (LLMs) as action proposers in reinforcement learning (RL) significantly boosts performance in text-based environments but incurs prohibitive computational costs. We introduce a cache-efficient framework for Bayesian RL that leverages LLM-derived action suggestions, drastically reducing these costs while maintaining near-optimal performance. Our approach features an adaptive caching mechanism, optimized via meta-learning based on policy performance, to enable efficient inference across text-based games (e.g., TextWorld, ALFWorld) and robotic control tasks (e.g., MuJoCo, MetaWorld). This framework achieves a 3.8×–4.7× reduction in LLM queries and 4.0×–12.0× lower median latencies (85–93ms on consumer hardware), while retaining 96–98% of the uncached policy’s performance. We provide theoretical guarantees on the reliability of cached decisions with Kullback-Leibler (KL) divergence bounds, which are validated empirically by high success rates (90.4–95.6%) in complex text environments. For offline RL, our proposed CQL-Prior variant improves performance by 14–29% and reduces training time by 38–40%. Evaluations across eight diverse tasks demonstrate the framework’s generalizability and practicality for resource-constrained settings, making LLM-guided RL a viable and accessible approach for both text-based and robotic applications.