The Transfer of Cognitive Paradigms.

Besides the HGF/MET and Wnt/β-catenin pathways, what other pathways are involved in ADT-induced DNPC? How are they related to each other?

This post clearly highlights the value present within the research community and the significance of enhancing its impact.

Crucially, the theoretical foundation underlying its methodology is being challenged by recent research. The paper uses reverse KL divergence as the core regularization term. However, recent studies have systematically rejected the intuition of simply transferring "mode-seeking" from generative models to reinforcement learning, showing that under common settings of low regularization strength and equivalent rewards, the KL-regularized objective itself leads to a unimodal optimal distribution, structurally suppressing output diversity [11]. This suggests that the claimed potential to "explore advanced, non-human reasoning paths" may be inherently constrained by the algorithm's own tendency toward "mode collapse." Consistently, a large body of empirical research also confirms that reinforcement learning training systematically induces "entropy collapse" in large language models, manifested as diminished output diversity, which is particularly pronounced in tasks requiring creativity.

Therefore, the phenomena observed in the paper, interpreted as the "emergence of reasoning ability" (such as the continuously increasing length of thought chains), need to be rigorously distinguished from another possibility: is this truly a deepening of the model's intelligence, or is it the model exhibiting saturation-style optimization within a limited pattern, or even falling into repetitive expression? The peer review process of the study has also pointed out that several of its design decisions lack corresponding empirical evidence, indicating that to support its core claims, more thorough methodological validation and controlled experiments are necessary (for example, ablation studies on the choice of regularization terms and components of the reward function) to rule out the above alternative explanations.

This may suggest that future research aiming to construct truly robust and creative reasoning models must directly address the challenge of entropy collapse. Promising approaches are beginning to emerge, including count-based intrinsic exploration rewards to encourage models to cover a broader solution space; or designing dynamic regularization strategies that balance reward optimization and distribution breadth during training. These ideas, emerging from the latest community research, offer more refined control tools over training the next generation of large language models than relying solely on KL regularization.

The reverse KL divergence DKL​(πθ​∥πref​)

Recently, Deep Seek has experienced several crashes. This is consistent with the situations analyzed in the paper; however, since there was no supporting evidence at the time, I removed this part from the published paper. The following is the content I deleted:

More importantly, the theoretical foundation on which its core algorithm design relies may itself be flawed. DeepSeek-R1 employs the GRPO algorithm, whose objective function explicitly includes the reverse KL divergence DKL​(πref​∥πθ​) as a regularization term. The paper adopts the common intuition from generative models, considering this design intended to constrain the policy from deviating excessively from the reference distribution. However, recent theoretical studies indicate that directly transferring the mode-covering/mode-seeking characteristics of forward/reverse KL to the reinforcement learning framework is a misconception. Research points out that in reward-based optimization, the primary tendency of any KL regularization is to induce mode collapse, with its behavior largely determined by the strength of regularization and the scale of rewards, rather than the direction of the KL divergence itself [11]. This implies that the regularization design relied upon by DeepSeek-R1 may be theoretically flawed and non-diverse by premise.

These concerns are not unfounded. A substantial body of empirical research in recent years has repeatedly confirmed that reinforcement learning training can systematically reduce the diversity of language model outputs, leading to the phenomenon known as entropy collapse [11]. This loss of diversity is broadly manifested across multiple dimensions, including formatting, random generation, creative exploration, and reasoning paths. Theoretical analyses further indicate that in policy gradient optimization, if an agent's action probabilities are highly correlated with their advantage values, entropy tends to decrease. This suggests that the tendency for chains of thought (CoT) in DeepSeek-R1 to be lengthy is likely not an isolated case, but rather a systemic risk inherent to the KL-regularized reinforcement learning framework it employs when faced with complex reward signals.

11.    Chen, A.G. et al.: KL-Regularized Reinforcement Learning is Designed to Mode Collapse. (2025) https://doi.org/10.48550/arXiv.2510.20817