The paper proposes that self‑improvement methods for language models—such as debate, bootstrap, and internal coherence maximization—can be unified under a framework called coherence optimization. This framework seeks a context‑to‑behavior mapping that is most compressible and jointly predictable, and the authors prove its equivalence to description‑length regularization. They further show that coherence regularization, when guided by a pretrained model prior, optimizes a lower bound on worst‑case accuracy for semi‑supervised learning, offering a theoretical explanation for the success of feedback‑free self‑improvement and predictions about its limits.
arXiv:2608. 05188v1 Announce Type: cross Abstract: Despite ever-increasing sophistication in language model (LM) pre- and post-training pipelines, many important failures persist: models overcondition on user framing ("sycophancy"), exhibit incomplete logical generalization, and produce confident but incorrect responses.
arXiv:2602. 04718v2 Announce Type: replace-cross Abstract: A central premise in mechanistic interpretability is that meaningful concepts in language models are represented by linear features in activation space.
arXiv:2606. 30609v1 Announce Type: cross Abstract: Sparse Autoencoders (SAEs) are widely used to interpret large language models by decomposing activations into sparse, human-understandable features, but scaling to large dictionaries exposes fundamental challenges.
arXiv:2602. 04718v4 Announce Type: replace Abstract: A central premise in mechanistic interpretability is that meaningful concepts in language models are represented by linear features in activation space.
The paper introduces On-Policy Self-Training, a method that allows large language models to improve themselves without external teachers or curated datasets. By generating its own question-answer pairs and privileged reasoning demonstrations guided only by coarse topic descriptions, the model creates a synthetic curriculum that enhances performance across multiple domains. Experiments show consistent self-improvement on both text and multimodal benchmarks, including Visual Question Answering, while preserving abilities on non-self-trained domains.
arXiv:2604. 13356v3 Announce Type: replace-cross Abstract: Mechanisms for continued self-improvement of language models without external supervision remain an open challenge.
The paper investigates how different training strategies affect the prompt sensitivity of large language models. It reproduces and compares methods such as refined data construction and robustness objectives, finding that while robustness fine‑tuning improves over standard fine‑tuning and in‑context learning, the prompt gap remains large (40–57%). Notably, newer techniques like CoIN and PPCL often underperform a simple data‑construction approach that uses one template per batch, and diagnostics suggest that mixed‑template batches force the optimizer to reconcile conflicting updates rather than learn a prompt‑agnostic representation.
arXiv:2607. 02460v1 Announce Type: cross Abstract: Post-training large language models (LLMs) without real-world interaction feedback or human-labeled supervision remains challenging, particularly in specialized domains where expert annotations are costly to obtain.
arXiv:2606. 05173v1 Announce Type: cross Abstract: Masked language modelling (MLM) has been the dominant pre-training objective for text encoders since BERT, yet it encourages representations that are strongly anchored to surface-form token identity rather than deeper semantic structure.
arXiv:2606. 08496v1 Announce Type: cross Abstract: Although Sparse Autoencoders (SAEs) have mitigated the opacity of large language models (LLMs) by decomposing dense representations into sparse features, explaining these features still remains a central challenge.
The paper proposes a self‑supervised framework that trains language models to predict concepts—sets of semantically equivalent tokens—rather than single tokens. This approach improves alignment with human similarity judgments, boosts performance on classification, clustering, and reranking tasks, and yields comparable or stronger downstream reasoning while lowering perplexity on semantically meaningful words and only slightly increasing overall perplexity.
arXiv:2607. 17558v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) offers a promising approach for training large language models without relying on a separate teacher model.