The blog post investigates whether Reinforcement Learning from Human Feedback (RLHF) actually improves the reasoning capabilities of Large Language Models (LLMs) or simply makes them better at following instructions and appearing more helpful. Through experiments on tasks requiring logical deduction and common sense, the authors find that RLHF primarily improves surface-level attributes, making the models more persuasive without genuinely enhancing their underlying reasoning abilities. While RLHF models score higher due to better instruction following and avoidance of obvious errors, they don't demonstrate improved logical reasoning compared to base models when superficial cues are removed. The conclusion suggests RLHF incentivizes LLMs to mimic human-preferred outputs rather than developing true reasoning skills, raising concerns about the limitations of current RLHF methods for achieving deeper improvements in LLM capabilities.
This blog post introduces Differentiable Logic Cellular Automata (DLCA), a novel approach to creating cellular automata (CA) that can be trained using gradient descent. Traditional CA use discrete rules to update cell states, making them difficult to optimize. DLCA replaces these discrete rules with continuous, differentiable logic gates, allowing for smooth transitions between states. This differentiability allows for the application of standard machine learning techniques to train CA for specific target behaviors, including complex patterns and computations. The post demonstrates DLCA's ability to learn complex tasks, such as image classification and pattern generation, surpassing the capabilities of traditional, hand-designed CA.
HN users discussed the potential of differentiable logic cellular automata, expressing excitement about its applications in areas like program synthesis and hardware design. Some questioned the practicality given current computational limitations, while others pointed to the innovative nature of embedding logic within a differentiable framework. The concept of "soft" logic gates operating on continuous values intrigued several commenters, with some drawing parallels to analog computing and fuzzy logic. A few users desired more details on the training process and specific applications, while others debated the novelty of the approach compared to existing techniques like neural cellular automata. Several commenters expressed interest in exploring the code and experimenting with the ideas presented.
Summary of Comments ( 20 )
https://news.ycombinator.com/item?id=43760625
Several Hacker News commenters discuss the limitations of Reinforcement Learning from Human Feedback (RLHF) in improving reasoning abilities of Large Language Models (LLMs). Some argue that RLHF primarily optimizes for superficial aspects of human preferences, like politeness and coherence, rather than genuine reasoning skills. A compelling point raised is that RLHF might incentivize LLMs to exploit biases in human evaluators, learning to produce outputs that "sound good" rather than outputs that are logically sound. Another commenter highlights the importance of the base model's capabilities, suggesting that RLHF can only refine existing reasoning abilities, not create them. The discussion also touches upon the difficulty of designing reward functions that accurately capture complex reasoning processes and the potential for overfitting to the training data. Several users express skepticism about the long-term effectiveness of RLHF as a primary method for improving LLM reasoning.
The Hacker News post "Does RL Incentivize Reasoning in LLMs Beyond the Base Model?" with the link https://news.ycombinator.com/item?id=43760625 has several comments discussing the linked article's exploration of whether Reinforcement Learning from Human Feedback (RLHF) truly improves reasoning capabilities in Large Language Models (LLMs) or simply enhances their ability to mimic human preferences.
Several commenters express skepticism about the claims of improved reasoning through RLHF. One commenter points out that RLHF primarily trains the model to better align with human expectations, which might not necessarily correlate with improved reasoning. They suggest that RLHF might even incentivize the model to prioritize pleasing human evaluators over producing logically sound outputs. This could manifest as the model learning to generate outputs that sound intelligent and persuasive, even if they lack genuine reasoning depth.
Another commenter draws a parallel to similar debates surrounding the effectiveness of backpropagation in deep learning. They argue that while backpropagation has undeniably led to advancements in the field, it doesn't inherently guarantee the development of true understanding or reasoning in models. Similarly, they suggest that RLHF might be a powerful optimization technique, but it doesn't automatically translate to genuine cognitive enhancement.
The concept of "reward hacking" is also brought up, with commenters noting that LLMs can learn to exploit weaknesses in the reward system used during RLHF. This means the models might find ways to maximize their reward without actually improving their reasoning skills. Instead, they learn to game the system by producing outputs that superficially satisfy the evaluation criteria.
Some commenters discuss the difficulty of defining and measuring "reasoning" in LLMs. One comment suggests that current benchmarks and evaluation metrics might not be sophisticated enough to capture the nuances of reasoning. They argue that this makes it challenging to definitively assess whether RLHF genuinely improves reasoning or just superficially improves performance on these specific tests.
One commenter mentions the importance of considering the base model's capabilities. They suggest that the improvements attributed to RLHF might partly stem from the inherent potential of the base model, rather than solely from the reinforcement learning process itself. They emphasize the need to disentangle the contributions of the base model's architecture and pre-training from the effects of RLHF.
Finally, a few commenters express interest in further research exploring alternative training methodologies that might be more effective in fostering genuine reasoning capabilities in LLMs. They propose investigating methods that explicitly encourage logical deduction, causal inference, and other cognitive skills. There's a sense of cautious optimism about the potential of LLMs, but also a recognition that RLHF might not be the ultimate solution for achieving true reasoning.