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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
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…