Ladder is a novel approach for improving large language model (LLM) performance on complex tasks by recursively decomposing problems into smaller, more manageable subproblems. The model generates a plan to solve the main problem, breaking it down into subproblems which are then individually tackled. Solutions to subproblems are then combined, potentially through further decomposition and synthesis steps, until a final solution to the original problem is reached. This recursive decomposition process, which mimics human problem-solving strategies, enables LLMs to address tasks exceeding their direct capabilities. The approach is evaluated on various mathematical reasoning and programming tasks, demonstrating significant performance improvements compared to standard prompting methods.
This paper explores cognitive behaviors that contribute to effective self-improvement in reasoning. It argues that simply possessing knowledge and logical rules isn't enough; individuals must actively engage in metacognitive processes to refine their reasoning. These processes include actively seeking out and evaluating evidence, considering alternative perspectives and explanations, identifying and correcting biases, and reflecting on one's own reasoning process. The authors propose a framework for these "self-improving reasoner" behaviors, emphasizing the importance of "epistemic vigilance," which involves carefully scrutinizing information and its sources, and "adaptive reasoning," which entails adjusting reasoning strategies based on performance and feedback. Ultimately, cultivating these cognitive behaviors is essential for overcoming limitations in reasoning and achieving more accurate and reliable conclusions.
HN users discuss potential issues and implications of the paper "Cognitive Behaviors That Enable Self-Improving Reasoners." Some express skepticism about the feasibility of recursive self-improvement in AI, citing the potential for unforeseen consequences and the difficulty of defining "improvement" rigorously. Others question the paper's focus on cognitive architectures, arguing that current deep learning approaches might achieve similar outcomes through different mechanisms. The limited scope of the proposed "cognitive behaviors" also draws criticism, with commenters suggesting they are too simplistic to capture the complexities of general intelligence. Several users point out the lack of concrete implementation details and the difficulty of testing the proposed ideas empirically. Finally, there's a discussion about the ethical implications of self-improving AI, highlighting concerns about control and alignment with human values.
Struggling with depression and a sense of aimlessness after dropping out of college, the author found solace and direction through Math Academy, an intensive summer program. The structured environment, challenging curriculum, and supportive community helped him rediscover his love for learning and build confidence. He credits the program with pulling him out of a dark place, fostering a sense of accomplishment, and ultimately setting him on a new path toward a fulfilling career in programming. The rigorous mathematical focus provided not just knowledge, but crucial problem-solving skills applicable beyond academia, reigniting his passion and giving him a renewed sense of purpose.
Hacker News users generally reacted positively to the original blog post. Several commenters shared similar experiences of feeling lost and directionless, echoing the author's "valley of despair." Some discussed the benefits of structured learning environments like Math Academy, particularly for those who thrive on rigorous intellectual challenges. Others praised the author's vulnerability and honesty. A few commenters questioned the accessibility and cost of such programs, suggesting alternative resources like community college or online courses. Some also debated the focus on "elite" institutions, advocating for broader access to quality education. Finally, a couple of users expressed skepticism about the long-term effectiveness of bootcamps in general, while acknowledging the author's positive experience.
Writing can be a powerful tool to break free from ingrained thought patterns and emotional defaults. By articulating our thoughts and feelings, we gain a conscious awareness of them, allowing us to examine and challenge their validity. This process of externalizing internal states creates distance, offering a fresh perspective and enabling more deliberate responses instead of automatic reactions. Through writing, we can explore alternative perspectives, rehearse new behaviors, and ultimately reprogram our "default settings" to align with our desired ways of thinking and being. It's a method of self-discovery and a pathway to personal growth, fostering greater emotional regulation and more intentional living.
HN users generally agreed with the premise that writing helps clarify thinking and escape ingrained patterns. Several pointed out that writing, especially for an audience, forces one to organize thoughts and articulate them clearly, revealing inconsistencies and prompting deeper consideration. Some emphasized the importance of revisiting and editing written work to further refine ideas. A few commenters mentioned specific benefits like improved decision-making and reduced stress through journaling or expressive writing. There's also discussion around various writing styles and tools, from morning pages to digital note-taking apps, that facilitate this process. However, some cautioned against over-reliance on writing as a solution and emphasized the importance of action alongside reflection.
Frustrated with excessive phone use, the creator developed "Touch Grass," an Android app designed to encourage breaks from screen time. The app uses GPS to confirm the user is physically outside and then starts a timer. Only after spending a user-defined amount of time outdoors will the app grant access to blocked apps, effectively locking the user out until they've "touched grass." This gamified approach aims to promote healthier digital habits and reconnect users with the real world.
Hacker News commenters generally found the "touch grass" app amusing, but impractical. Several questioned the effectiveness of physically touching grass through a phone screen, noting the inherent irony and missing the point of the idiom. Some suggested improvements like requiring a photo of actual grass or GPS verification of being outdoors. Others highlighted the performative nature of the app, comparing it to other avoidance techniques. A few commenters appreciated the humor and simple execution, viewing it as a lighthearted take on the problem of doomscrolling. Some pointed out the potential for addictive gamification of "touching grass" itself. Overall, the consensus leaned towards the app being a fun, albeit slightly absurd, commentary on internet overuse rather than a serious solution.
The author describes how they inadvertently trained themselves to equate effort with negative outcomes. Starting with a challenging physics class, they developed a belief that trying hard and still failing was worse than not trying at all. This self-protective mechanism spread to other areas of their life, leading to procrastination and avoidance of difficult tasks. Eventually, they recognized this pattern of self-sabotage and began the process of unlearning it by reframing failure as a necessary step in learning and growth, and focusing on the process rather than solely on outcomes. They began tackling challenging tasks, celebrating small victories, and gradually rebuilding their self-confidence.
HN commenters largely agreed with the author's premise that negative self-talk and a focus on potential failure can become a self-fulfilling prophecy. Several shared similar experiences of psyching themselves out or developing learned helplessness. Some suggested techniques to combat this, including cognitive behavioral therapy (CBT), positive self-talk, and focusing on small wins. One commenter pointed out the link between the article's concept and the idea of "locus of control," emphasizing the importance of feeling agency over one's actions. Another questioned the framing of "conditioning," suggesting it implied a more passive process than the conscious, albeit negative, choices described. A few comments also discussed the potential evolutionary basis for negativity bias and its role in risk avoidance.
The concept of "minimum effective dose" (MED) applies beyond pharmacology to various life areas. It emphasizes achieving desired outcomes with the least possible effort or input. Whether it's exercise, learning, or personal productivity, identifying the MED avoids wasted resources and minimizes potential negative side effects from overexertion or excessive input. This principle encourages intentional experimentation to find the "sweet spot" where effort yields optimal results without unnecessary strain, ultimately leading to a more efficient and sustainable approach to achieving goals.
HN commenters largely agree with the concept of minimum effective dose (MED) for various life aspects, extending beyond just exercise. Several discuss applying MED to learning and productivity, emphasizing the importance of consistency over intensity. Some caution against misinterpreting MED as an excuse for minimal effort, highlighting the need to find the right balance for desired results. Others point out the difficulty in identifying the true MED, as it can vary greatly between individuals and activities, requiring experimentation and self-reflection. A few commenters mention the potential for "hormesis," where small doses of stressors can be beneficial, but larger doses are harmful, adding another layer of complexity to finding the MED.
Agnes Callard's Open Socrates offers a practical philosophy focused on "aspiring." Callard argues that we should actively strive for values we don't yet hold, embracing the difficult process of becoming the kind of person who embodies them. The book explores this through engaging with figures like Socrates and Plato, emphasizing the importance of self-creation and the pursuit of a life guided by reason and critical thinking. While not providing easy answers, it encourages readers to confront their own limitations and actively work towards a better version of themselves.
HN commenters generally express interest in Callard's approach to philosophy as a way of life, rather than just an academic pursuit. Several praise the reviewer's clear explanation of Callard's "aspirational" philosophy. Some discuss their own experiences with transformational learning and self-improvement, echoing Callard's emphasis on actively striving for a better self. A few express skepticism about the practicality or accessibility of her methods, questioning whether her approach is truly novel or simply repackaged ancient wisdom. Others are intrigued by the concept of "proleptic reasons," where present actions are justified by a future, hoped-for self. Overall, the comments reflect a mix of curiosity, cautious optimism, and some doubt regarding the applicability of Callard's philosophical framework.
Habby is a minimalist digital bullet journal combining journaling and habit tracking. It offers a clean, distraction-free interface for daily note-taking and progress monitoring on personal habits. Users can create and track habits, write daily journal entries, and review their progress visually. The focus is on simplicity and ease of use, providing a streamlined approach to personal organization and self-improvement.
HN users generally praised Habby's simplicity and clean design, finding it a refreshing alternative to overly complex habit trackers. Several commenters appreciated the focus on privacy, with the app storing data locally. Some suggested potential improvements, such as customizable reminders, exporting data, and the ability to track more nuanced habits beyond simple checkmarks. The developer responded to several comments, indicating openness to feedback and future development. There was also a brief discussion comparing Habby to similar apps like Streaks.
Ron Garrett reflects on six failed startup attempts, rejecting the label of "failure" and instead focusing on the valuable lessons learned. He emphasizes the importance of choosing the right co-founder, validating ideas early and often, building a minimum viable product (MVP) quickly, and iterating based on user feedback. Marketing and distribution proved crucial, and while passion is essential, it must be coupled with a realistic market and sustainable business model. Ultimately, he learned that "failing fast" and adapting are key to entrepreneurial growth, viewing each setback as a stepping stone toward future success.
HN commenters largely praised the author's vulnerability and honesty in sharing their startup failures. Several highlighted the importance of recognizing sunk cost fallacy and knowing when to pivot or quit. Some questioned the framing of the experiences as "failures," arguing that valuable lessons and growth emerged from them. A few commenters shared their own similar experiences, emphasizing the emotional toll of startup struggles. Others offered practical advice, such as validating ideas early and prioritizing distribution. The prevailing sentiment was one of empathy and encouragement, acknowledging the difficulty of entrepreneurship and the courage it takes to try repeatedly.
Summary of Comments ( 65 )
https://news.ycombinator.com/item?id=43287821
Several Hacker News commenters express skepticism about the Ladder paper's claims of self-improvement in LLMs. Some question the novelty of recursively decomposing problems, pointing out that it's a standard technique in computer science and that LLMs already implicitly use it. Others are concerned about the evaluation metrics, suggesting that measuring performance on decomposed subtasks doesn't necessarily translate to improved overall performance or generalization. A few commenters find the idea interesting but remain cautious, waiting for further research and independent verification of the results. The limited number of comments indicates a relatively low level of engagement with the post compared to other popular Hacker News threads.
The Hacker News post titled "Ladder: Self-improving LLMs through recursive problem decomposition" (https://news.ycombinator.com/item?id=43287821) discussing the arXiv paper (https://arxiv.org/abs/2503.00735) has a modest number of comments, generating a brief but interesting discussion.
Several commenters focus on the practicality and scalability of the proposed Ladder approach. One commenter questions the feasibility of recursively decomposing problems for real-world tasks, expressing skepticism about its effectiveness beyond toy examples. They argue that the overhead of managing the decomposition process might outweigh the benefits, particularly in complex scenarios. This concern about scaling to more intricate problems is echoed by another user who points out the potential for exponential growth in the number of sub-problems, making the approach computationally expensive.
Another line of discussion revolves around the novelty of the Ladder method. One commenter suggests that the core idea of recursively breaking down problems is not entirely new and has been explored in various forms, such as divide-and-conquer algorithms and hierarchical reinforcement learning. They question the extent of the contribution made by this specific paper. This prompts a response from another user who defends the paper, highlighting the integration of these concepts within the framework of large language models (LLMs) and the potential for leveraging their capabilities for more effective problem decomposition.
Furthermore, the evaluation methodology is brought into question. A commenter notes the reliance on synthetic benchmarks and expresses the need for evaluation on real-world datasets to demonstrate practical applicability. They emphasize the importance of assessing the robustness and generalization capabilities of the Ladder approach beyond controlled environments.
Finally, a few commenters discuss the broader implications of self-improving AI systems. While acknowledging the potential benefits of such approaches, they also express caution about the potential risks and the importance of careful design and control mechanisms to ensure safe and responsible development of such systems.
While the discussion is not extensive, it touches upon key issues related to the feasibility, novelty, and potential impact of the proposed Ladder method, reflecting a balanced perspective on its strengths and limitations.