35 citations · 243 across the 61 of their papers we have counts for
17 papers · 1 filter
The Weakest Link: Distilling LLM Reasoning with Worst-Case Constrained Reinforcement Learning
Matthieu Zimmer, Xiaotong Ji, Tu Nguyen +1
Distilling the reasoning capabilities of large language models (LLMs) into smaller students is a central challenge for efficient deployment. Current approaches face a fundamental t…
Does This Action Still Explain the Task? Reverse Scoring for Diffusion Language Model Agents
Jiacheng Qiu, Christopher E. Mower, Jan Peters +2
Diffusion-based large language models (dLLMs) promise to break the sequential latency bottleneck of autoregressive agents through parallel decoding, but recent evaluations show thi…
Tail-Influence Sampling for CVaR Policy Evaluation
Pauline Bourigault, Xiaotong Ji, Matthieu Zimmer +2
Policies with similar mean returns can differ sharply in rare failures, yet estimating lower-tail conditional value-at-risk (CVaR) accurately can require many costly rollouts. When…
Beam Search as Test-Time Self-Distillation via Counterfactual Contexts
Su Ee Tan, Xiaotong Ji, Rasul Tutunov +2
Self-Distillation Fine-Tuning (SDFT) enables a language model to act as its own teacher: by conditioning on a demonstration, the model produces an implicit reward via pointwise mut…
Composable Decoding on the Probability Simplex: Theory and Implementation
Xiaotong Ji, Ahmed Khaled Khamis, Rasul Tutunov +2
Decoding for large language models is typically treated as a collection of isolated sampling strategies, with limited theoretical understanding of the behaviours they induce and ho…
iSDFT: Information-Proximal Self-Distillation for Continual Learning in LLMs
Ahmed Khaled Khamis, Xiaotong Ji, Hassan Jaber +4
On-policy self-distillation fine-tuning (SDFT) learns new skills from demonstrations while reducing forgetting, but it always distils toward the full demonstration-conditioned teac…
Trust Region Inverse Reinforcement Learning: Explicit Dual Ascent using Local Policy Updates
Anish Diwan, Davide Tateo, Christopher E. Mower +3
Inverse reinforcement learning (IRL) is typically formulated as maximizing entropy subject to matching the distribution of expert trajectories. Classical (dual-ascent) IRL guarante…
HapTile: A Haptic-Informed Vision-Tactile-Language-Action Dataset for Contact-Rich Imitation Learning
Amirhosein Alian, Yongqiang Zhao, Shiyi Gu +5
Despite the importance of tactile sensing for reliable manipulation, most existing Vision-Language-Action (VLA) datasets remain vision-only, and those that do incorporate tactile i…
EmbodimentSemantic: A Spatial Scene-Graph Dataset and Benchmark for Vision-Language Models on Embodied Manipulation Trajectories
Hassan Jaber, Refinath S N, Luca Cagliero +2
Spatial grounding remains a key limitation of vision-language-action (VLA) systems for robotic manipulation. While current models can recognize objects and follow language instruct…
Robots Need More than VLA and World Models
Elis Karcini, Faisal Mehrban, Quang Nguyen +6
Generalist robot intelligence is often framed as a policy-scaling problem: collect more robot demonstrations, train larger Vision-Language-Action (VLA) models, and expect broader g…
Risk-Controlled Lean-as-Judge for Natural-Language Mathematical Reasoning
Pauline Bourigault, Xiaotong Ji, Matthieu Zimmer +2
Lean is increasingly used to judge natural-language mathematical answers, but its signal is partial: many answers never formalize, and a failed proof may reflect an ill-typed state…
Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial
Zhongwei Yu, Rasul Tutunov, Alexandre Max Maraval +13
Traditional scientific discovery relies on an iterative hypothesise-experiment-refine cycle that has driven progress for centuries, but its intuitive, ad-hoc implementation often w…
The -Combinator for LLMs: Solving Long-Context Rot with -Calculus
Amartya Roy, Rasul Tutunov, Xiaotong Ji +2
LLMs are increasingly used as general-purpose reasoners, but long inputs remain bottlenecked by a fixed context window. Recursive Language Models (RLMs) address this by externalisi…
Why the Brain Consolidates: Predictive Forgetting for Optimal Generalisation
Zafeirios Fountas, Adnan Oomerjee, Haitham Bou-Ammar +2
Standard accounts of memory consolidation emphasise the stabilisation of stored representations, but struggle to explain representational drift, semanticisation, or the necessity o…
Decoding as Optimisation on the Probability Simplex: From Top-K to Top-P (Nucleus) to Best-of-K Samplers
Xiaotong Ji, Rasul Tutunov, Matthieu Zimmer +1
Decoding sits between a language model and everything we do with it, yet it is still treated as a heuristic knob-tuning exercise. We argue decoding should be understood as a princi…
Scalable Power Sampling: Unlocking Efficient, Training-Free Reasoning for LLMs via Distribution Sharpening
Xiaotong Ji, Rasul Tutunov, Matthieu Zimmer +1
Reinforcement learning (RL) post-training is a dominant approach for improving the reasoning performance of large language models (LLMs), yet growing evidence suggests that its gai…
A Brain-like Synergistic Core in LLMs Drives Behaviour and Learning
Pedro Urbina-Rodriguez, Zafeirios Fountas, Fernando E. Rosas +5
The independent evolution of intelligence in biological and artificial systems offers a unique opportunity to identify its fundamental computational principles. Here we show that l…