1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.CL2026
Likelihood-Based Reward Designs for General LLM Reasoning
Ariel Kwiatkowski, Natasha Butt, Ismail Labiad +2
Fine-tuning large language models (LLMs) on reasoning benchmarks via reinforcement learning requires a specific reward function, often binary, for each benchmark. This comes with t…
cs.CL2025★ 1 cited
Soft Tokens, Hard Truths
Natasha Butt, Ariel Kwiatkowski, Ismail Labiad +2
The use of continuous instead of discrete tokens during the Chain-of-Thought (CoT) phase of reasoning LLMs has garnered attention recently, based on the intuition that a continuous…
cs.LG2025★ 1 cited
Outcome-based Exploration for LLM Reasoning
Yuda Song, Julia Kempe, Remi Munos
Reinforcement learning (RL) has emerged as a powerful method for improving the reasoning abilities of large language models (LLMs). Outcome-based RL, which rewards policies solely…