Role-Play Paradox in Large Language Models: Reasoning Performance Gains and Ethical Dilemmas (2024 · 9 citations)
Role-play prompting, where an LLM is told to act as a teacher, scriptwriter, or even an object like a coin, is known to improve reasoning.
Tiny Budgets, Big Gains: Parameter Placement Strategy in Parameter Super-Efficient Fine-Tuning (2025 · 8 citations)
Parameter-efficient fine-tuning methods such as LoRA lower the cost of adapting large models. Yet models now reach hundreds of billions of parameters, so even rank-1 LoRA can be too heavy for mobile hardware or labs with little compute. The authors ask how a very small, fixed budget of trainable parameters should be…
No More Stale Feedback: Co-Evolving Critics for Open-World Agent Learning (2026 · 3 citations)
Critique-guided reinforcement learning supplements sparse outcome rewards for LLM agents with natural-language feedback.
Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling (2026)
As LLMs turn into agents that call tools over many turns, reward models must judge entire interaction trajectories, not just single replies.

Co-authors here

12 of 16 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

Papers of our library cite their works 19 times (of the 149,439 papers whose bibliographies we read); 9 citing papers with a page here →.

Updated 2 November 2026