Title: Latent Principle Discovery for Language Model Self-Improvement
Venue: NeurIPS 2025
Accepted: yes
Keywords: self-improvement

language models

self-correction

principles

latent chain-of-thought
Abstract: When language model (LM) users aim to improve the quality of its generations, it is crucial to specify concrete behavioral attributes that the model should strive to reflect. However, curating such principles across many domains, even non-exhaustively, requires a labor-intensive annotation process. To automate this process, we propose eliciting these latent attributes that guide model reasoning toward human-preferred responses by explicitly modeling them in a self-correction setting. Our approach mines new principles from the LM itself and compresses the discovered elements to an interpretable set via clustering. Specifically, we employ a form of posterior-regularized Monte Carlo Expectation-Maximization to both identify a condensed set of the most effective latent principles and teach the LM to strategically invoke them in order to intrinsically refine its responses. We demonstrate that bootstrapping our algorithm over multiple iterations enables smaller language models (7-8B parameters) to self-improve, achieving +8-10\% in AlpacaEval win-rate, an average of +0.3 on MT-Bench, and +19-23\% in principle-following win-rate on IFEval. We also show that clustering the principles yields interpretable and diverse model-generated constitutions while retaining model performance. The gains that our method achieves highlight the potential of automated, principle-driven post-training recipes toward continual self-improvement.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces STaPLe (Self-Taught Principle Learning), a method designed to enable self-improvement of language models (LMs). The core idea behind STaPLe is that a model can automatically discover and learn latent principles (rules or attributes) that help it improve its responses. STaPLe employs an Expectation-Maximization (EM) algorithm consisting of two main steps: E-step (principle discovery) and M-step (principle learning). In the E-step, the model uses rejection sampling to identify potential principles and improve its responses, while the M-step fine-tunes the model based on the discovered principles. The paper demonstrates that STaPLe outperforms existing approaches like STaR in various benchmarks (MT-Bench, AlpacaEval, IFEval), showing that self-improvement based on latent principles is an effective strategy for enhancing LM performance.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\nSTaPLe makes contributions in principle-augmented fine-tuning, which is both timely and important, by leveraging EM algorithm to enable iterative learning of principles and response refinement. This approach allows the model to improve its performance by learning principles automatically, differentiating it from methods that rely on predefined principles. Another key strength of STaPLe is its use of posterior regularization via clustering, which compresses the learned principles while maintaining their interpretability. This process helps to make the principles more understandable to humans, enhancing transparency. Additionally, STaPLe’s agnostic approach to similarity metrics offers flexibility, allowing the model to use various metrics for performance improvement, making it adaptable to different tasks and ensuring robust performance across various use cases.\n\n**Weaknesses**\n\n1. **Reliance on another type of human involvement.** The authors claim that their approach aims to avoid conventional forms of human involvement, such as relying on “human annotations distinguishing between a chosen and rejected generation” or “human-curated constitutions.” However, their proposed method fundamentally depends on gold responses provided by users, another form of human involvement. Given this, the manuscript lacks a clear justification of why requiring gold responses from users is practically more efficient, scalable, or less burdensome compared to the forms of human involvement it aims to replace. Without this, the practical usage and broader applicability of the proposed method could be limited, as it merely substitutes one form of human dependence with another.\n2. **Insufficient positioning and relevant baseline.** The positioning (Section 2) could be improved by explicitly discussing the similarities and differences between STaPLe and closely related approaches, especially Findeis et al. (2025). In my view, both works aim at deriving latent principles for aligning language models, but differ in their learning source (and of course, the methodology): STaPLe derives them from gold responses, whereas Findeis et al. derive them from pairwise comparisons. In addition, given the first weakness (reliance on gold responses), it would be helpful to empirically evaluate Findeis et al.’s comparison-based approach as an additional baseline. Such an evaluation would clarify the efficiency trade-offs and practical advantages of deriving latent principles from gold responses versus pairwise comparisons.\n3. **Analysis of representatives.** While the clustering mechanism in STaPLe reduces the number of unique principles, it is unclear whether the compressed principles effectively *represent* the original set. The analysis demonstrates compression but lacks a direct evaluation of *representativeness*, which is essential to confirm that the reduced set still captures all critical principles for improved interpretability.\n\n\n**Minor comments**\n\n1. The method description relies heavily on numerous equations, which may be difficult for a broader audience to fully grasp. It would be beneficial to include an illustrative figure that shows the effect of each process, as the currently included figure only demonstrates the overall process flow and does not highlight the impact or outcome of each step in the method.\n2. (Very minor) It would improve readability if there were a direct comparison table showing performance based on different similarity scores. Currently, the tables for each similarity score are presented separately (one in the main text and another in the appendix), which makes it harder to quickly compare them. Combining this information in a single table could enhance clarity.\n\n**References**\n\n- Findeis et al. (2025). “Inverse Constitutional AI: Compressing Preferences into Principles.”\n\nQUESTIONS:\nPlease see weaknesses.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-11"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a fully automated \"principle-driven\" self-improvement framework - Self-Taught Principle Learning (STaPLe): let the small language model (7-8 B) discover the \"latent principles\" that can improve the quality of answers, and then learn how to call these principles during reasoning to criticize and revise its initial answers, thereby achieving continuous self-improvement.\n\nDuring each iteration, the model (i) drafts an answer $𝑦_1$, (ii) samples candidate principles 𝑧 given the gold answer $𝑦^G$, (iii) produces revised answers $𝑦^2$  that pass a rejection filter, and (iv) fine-tunes itself to invoke the selected 𝑧 before answering. A hierarchical clustering step keeps only one medoid principle per cluster, yielding a compact, human-readable “constitution”.\n\nFour iterations on mixed alignment data improve MT-Bench and AlpacaEval scores by 15-23 pp over the base model and surpass STaR / Self-Refine baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\nConceptual novelty – views principles as latent variables and optimises them via MC-EM, unifying discovery ↔ execution.\n\nFull automation – removes manual constitution engineering, lowering domain-transfer cost.\n\nInterpretability – hierarchical clustering keeps a 50 % subset of principles while matching performance, yielding a concise rule set.\n\nConsistent gains – +19-23 pp on AlpacaEval-2.0 and +0.11-0.18 on MT-Bench across three distinct 7-8 B backbones.\n\nWeaknesses\n\nI can understand the quantitative definition of the potential principle $z$, but I doubt whether this principle can be obtained only through prompts. In addition, I doubt that the principle of screening by the similarity of $y^i$ and $y^G$ is not necessarily accurate, after all, this paper starts from the reasoning of \"principles\". Through similarity screening, I doubt that the principles learned are some \"plagiarism\" or \"specific and non-generalizable\" \"detailed guidance\" rather than principles. There should be a more accurate evaluation model, etc., for a deeper analysis.\n\nThe paper is only verified on Llama-3, Granite, and Qwen at the 7–8B level, and has not tested the migration of these principles to larger models. Therefore, there is uncertainty in scale extrapolation, and rediscovery/clustering may be required to match stronger representations.\n\nThe author gives a visualization of the \"constitution\" generated after the iteration of the three models in the appendix, and uses the convergence of cluster size (~50% compression) to show that the principle set tends to be stable, but only qualitative examples are given for \"which principles are most effective\", lacking systematic statistics and further analysis.\n\nQUESTIONS:\nFundamental Questions:\n\n1. The paper is entirely based on fine-tuning (SFT), and no experiments were conducted on the RAG of \"retrieval of principles and splicing them during reasoning\". This reminds me that this paper is similar to **MetaMind: Modeling Human Social Thoughts with Metacognitive Multi-Agent Systems**, both of which let the model \"propose hypothesis → self-revise\". STaPLe builds a single model + Monte-Carlo EM, and learns to \"call principles\" and internalize them through SFT. MetaMind builds a multi-agent loop with explicit metacognitive control. Therefore, STaPLe can be seen as a \"single-model version of principle bootstrapping\", providing another perspective of non-multi-agent cooperation. I am curious about the comparison of the effects of explicit enhancement and fine-tuning internalization under the same setting. It will be beneficial to compare with this work to discover RAG vs. SFT. You can also try retrieving $top-𝑘$ principles at inference instead of SFT. Even a small comparison would clarify when full fine-tuning is necessary.\n\n2. When comparing the method proposed in this paper with the baseline method, does it consider the difference in iterative convergence rounds of different methods?\n\n3. The author emphasizes that the algorithm is also applicable to \"unverifiable\" tasks, because the similarity function can be replaced by Rouge-L or LLM-as-judge scores, and principle learning still uses \"making the answer closer to the reference\" as a signal. Is this strongly related to the reward rule setting of reinforcement learning? Further exploration is recommended.\n\nResearch Questions:\n\n1. Could you demonstrate STaPLe on tasks without explicit references, using LLM-judged pairwise wins as $𝑓(⋅)$? A convincing result would strengthen Significance.\n\n2. A quantitative breakdown (factuality, style, safety, etc.) of the final medoids would improve interpretability claims.\n\n3. If time allows, test the clustered constitution on a larger 34 B model; showing gains would boost my Significance rating.\n\nLIMITATIONS:\nPrinciple sampling uses $𝑦^𝐺$; scalability to settings without references (e.g. open-ended creative tasks) is only sketched.\n\nThe author admits that the principle is only effective for improving a subset of samples, and if the gold standard noise or malicious targets will lead to self-degradation, human review is required. Author also did not analyse potential bias amplification when principles encode cultural preferences.\n\nNo theoretical analysis is given for \"data sufficiency\", and it mainly relies on experience curves, so generalization still needs to be verified by more tasks.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThese principles are treated as latent variables in an Expectation-Maximization-like process. At each iteration, the model generates better answers by conditioning on the gold answer (hinting), extracts candidate principles, filters them using similarity metrics (like Rouge-L or LLM judges), clusters them into a distilled constitution, and fine-tunes on the best ones. Over four iterations, the method shows consistent performance improvements on multiple benchmarks (MT-Bench, AlpacaEval, IFEval) for several open-source 7–8B models, outperforming simple self-refinement and STaR-style baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nThe work shows that small open models can bootstrap themselves without external teachers. Modelling principles as latent variables distinguishes the method from prior self-refinement approaches, and the principled EM derivation plus a clustering-based regulariser add technical depth . The experimental section is relatively thorough with ablations on clustering and scoring functions.\n\nWeakness:\n1.\tThe model gets a hint by seeing the correct answer while generating principles in the propised method.  What if this risks making the model dependent on that answer that it might just learn to copy parts of it rather than actually reason towards it.  Also, the authors say that they filter out poor candidates, but they don’t hsow how often the golden answer leaks into the principles of how much this affect the performance. \n2.\tThe paper frames the method as EM-learning but the scoring presented in the paper doesn’t seem trainable.  Can you clarify on this?\n3. the clustering step is happening after principles are generated and filtered which means, its not part of the training process if I understand it correct.  Also the clustering threshold is tuned using another optimizer?  Can you explain?\n4.\tThe paper compares against basic self-refinement and instruction tuned models, but no results on newer methods like RISE.\n5.\tDo you have experiments on how the model generated principles could introduce biases and errors?\n\nQUESTIONS:\nPlease refer to the weakness for questions\n\nLIMITATIONS:\nPlease address the questions in the weakness section", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a framework for self-refinement of large language models.\n\nThey use the model itself prompted with a gold response to generate principles (in natural language) and critique its own initial response to a input against that generated principle, and then generate a refined response based on the critique. \n\nThese refined responses are used with supervised fine-tuning to self-improve the model to align to the self-generated principles of a good response derived from the model by looking at the gold response. \n\nThe paper poses this solution as an EM setup given the iterative self-improvement where the prompting approximates the estimation.\n\nThey also empirically arrive at clustering the generated principles (natural language) embeddings to get a reduced set for interpretability.\n\nSTRENGTHS AND WEAKNESSES:\nWeakness\n1. The paper is posing things as EM/posterior estimation but approximating or choosing empirical setup, the connection is not very relevant. The EM is sparsely related because its repeatedly fine-tune (maximizing step to generate critiques and responses for fine-tuning, and then the learning step, to SFT optimize with it). Figure 2.\n\n2. Evaluation: The baseline is considered with fine-tuning on 50k gold responses but the proposed method uses the 50k with 4 additional iterations each in which 10k gold responses are used to generate critiques and responses to fine-tune with. A fair baseline would be to use all the 50k + 40k input-gold response pairs in SFT which is not considered in the paper.\n\n3. Setup does not seem well motivated: The setup is not very useful - Why would we use Gold supervision to explicitly generate critiques vs just using SFT with them and implicitly learning it? Other than interpretability of the clustered generated set of principles, there is no reason it is not already implicit in the learning. Also, using gold annotations is more expensive - RLHF with preference annotation would be cheaper to collect. It would align the model without explicit principles. The explicit principles do not seem very useful. The paper also does not compare with RLHF.\n\nQUESTIONS:\nNone\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 1, "ORIGINALITY": 2, "DATE": "2025-07-06"}