A recent arXiv preprint, titled "Sparse RL Relaxation for LLM Reasoning," addresses the growing interest in soft Chain-of-Thought (CoT) reasoning. Instead of relying on discrete tokens, this line of work uses continuous representations, allowing a mixture of token embeddings to potentially capture richer intermediate reasoning states.
The paper's abstract notes that this approach is motivated by the idea that a mixture of token embeddings can represent... (the text cuts off). The authors propose a sparse reinforcement learning relaxation as a way to train LLMs with these continuous reasoning paths, though the abstract does not detail the method or results.
As the abstract is brief, the full scope of the contribution remains unclear. However, the work signals a continued push toward more flexible reasoning representations in large language models, potentially offering an alternative to standard discrete-token CoT.