1 citations · 1 across the 20 of their papers we have counts for
6 papers · 1 filter
Explorer-Zero: Improving Self-Play for Long-Horizon Tasks with Decomposable Verifiable Rewards
Tianyu Liu, Davide Belli, Bence Major +2
Training LLM agents on long-horizon interactive tasks with reinforcement learning typically relies on human-annotated tasks and reliable verification, both expensive to construct f…
When Cloud Agents Meet Device Agents: Lessons from Hybrid Multi-Agent Systems
Corrado Rainone, Davide Belli, Bence Major +1
The design space of agentic AI inference spans two extremes: frontier large language models (LLMs), typically hosted in the cloud and offering strong performance across a wide rang…
LaneRoPE: Positional Encoding for Collaborative Parallel Reasoning and Generation
Gabriele Cesa, Thomas Hehn, Aleix Torres-Camps +4
Parallel LLM test-time scaling techniques (e.g., best-of-) require drawing sequences conditioned on the same input prompt. These methods boost accuracy while exploiting th…
Memory-Efficient Looped Transformer: Decoupling Compute from Memory in Looped Language Models
Victor Conchello Vendrell, Arnau Padres Masdemont, Niccolò Grillo +3
Recurrent LLM architectures have emerged as a promising approach for improving reasoning, as they enable multi-step computation in the embedding space without generating intermedia…
Reasoning as Compression: Unifying Budget Forcing via the Conditional Information Bottleneck
Fabio Valerio Massoli, Andrey Kuzmin, Arash Behboodi
\ac{CoT} prompting improves LLM accuracy on complex tasks but often increases token usage and inference cost. Existing ``Budget Forcing'' methods reduce cost via fine-tuning with h…
LUMINA: Long-horizon Understanding for Multi-turn Interactive Agents
Amin Rakhsha, Thomas Hehn, Pietro Mazzaglia +3
Large language models can perform well on many isolated tasks, yet they continue to struggle on multi-turn, long-horizon agentic problems that require skills such as planning, stat…