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Training LLM agents with reinforcement learning (RL) is bottlenecked by environments, which must provide verifiable rewards, support long-horizon interaction, and scale cheaply. Existing approaches rely on costly human-curated data or on LLM-generated environments that risk hallucinations and benchmark contamination. W...
Mixture-of-Experts (MoE) architectures decouple model capacity from computational cost, yet incur high memory footprints as parameters grow linearly with the number of experts. Recurrent Transformers achieve parameter efficiency by reusing layer weights, but typically lack the capacity for competitive language modeling...
Kevo is a co-evolving framework that jointly learns to construct a structured knowledge base and reason over it for knowledge-intensive question answering, which leads to larger, better-connected knowledge structures with higher answer reachability, while also improving compositional factual reasoning and controllabili...
This work proposes continuous-query LMLM (CO-LMLM), where the KB pairs continuous keys with textual knowledge values, a significant departure from prior reliance on relational KB and queries, which outperforms prior LMLMs and vanilla LLMs in both perplexity and factual precision.
Optizing Your Sampling (OYS), which instead treats timestep selection as a black-box optimization problem, optimizing the target metric directly with Bayesian optimization, improves both simple and sophisticated samplers such as Euler and DPM-Solver++.
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