Author

Xiao-Tong Ji

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#artificial intelligence Preprint Sep 2026

The Weakest Link: Distilling LLM Reasoning with Worst-Case Constrained Reinforcement Learning

Distilling the reasoning capabilities of large language models (LLMs) into smaller students is a central challenge for efficient deployment. Current approaches face a fundamental tension: optimizing purely for verifiable task rewards (e.g., via GRPO) leads to reward hacking, where students arrive at correct final answe...

Matthieu Zimmer, Xiao-Tong Ji, Tu Nguyen et al. · 0 citations

#machine learning Review Sep 2026

Tail-Influence Sampling for CVaR Policy Evaluation

A tail influence for each queryable conditional law that aggregates how its uncertainty affects CVaR across every Bellman reuse is derived, and an exact-grid regime in which tail- and mean-optimal allocations coincide is characterized.

P. Bourigault, Xiao-Tong Ji, Matthieu Zimmer et al. · 1 citation

#artificial intelligence Preprint Sep 2026

Beam Search as Test-Time Self-Distillation via Counterfactual Contexts

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 mutual information, which guides on-policy learning without external supervision. However, SDFT operates at training time: it requires gradient...

Su-Ee Tan, Xiao-Tong Ji, Rasul Tutunov et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Composable Decoding on the Probability Simplex: Theory and Implementation

CompoSimplex is introduced, a library with configurable support rules, regularisation primitives, and simplex solvers for constructing and evaluating compositional decoders, and it is shown that compositions can realise trade-offs between single-sample quality, multi-sample quality, and diversity that are not attained...

Xiao-Tong Ji, Ahmed Khaled Khamis, Rasul Tutunov et al. · 0 citations

#artificial intelligence Preprint Sep 2026

iSDFT: Information-Proximal Self-Distillation for Continual Learning in LLMs

Controling how much and when teacher information is introduced improves specialisation while preserving broader capability is shown, and results show that controlling how much and when teacher information is introduced improves specialisation while preserving broader capability.

Ahmed Khaled Khamis, Xiao-Tong Ji, Hassan Jaber et al. · 0 citations

Jul 2026

Safe Reinforcement Learning under Regularized Probabilistic Counterexample Guidance

A counterexample-guided reinforcement learning method that navigates safe exploration in autonomous systems without prior knowledge, even when safety and optimality conflict, and a novel belief-based regularization method to address the distributional shift between online and offline learning and to balance optimizatio...

Xiao-Tong Ji, Antonio Filieri · ACM Transactions on Autonomo... · 0 citations