The Rise and Fall of Reinforcement Learning for LLM “Reasoning”

Thanks for reading and for your reply. However, you are overlooking key findings in the research that clearly indicate a lack of actual ‘reasoning’ or ‘thinking’. It’s important to recognize that your framing reflects anthropomorphism, a cognitive bias that attributes human traits to AI.

While reinforcement learning introduces a different signal compared to next-token prediction during pre-training, the underlying backpropagation mechanism used in post-training remains unchanged. Adjusting weights through this process does not amount to cognitive reasoning. The transformer’s behavior during inference also remains the same. Post-training is more accurately described as ‘stochastic funneling’ or ‘manifold sculpting’, modeling existing distributions to favor the examples seen during fine-tuning, based on the data and tasks involved.