Self-Refinement and Self-Consistency

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• Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
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• LLM Post-Training: A Deep Dive into Reasoning Large Language Models
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• Learning to Discover at Test Time
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• Learning to Reason for Factuality
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• Let’s Verify Step by Step
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• Looking beyond the next token
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• Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing
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• Mechanisms of Introspective Awareness
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• Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge
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• Metacognitive Retrieval-Augmented Large Language Models
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• Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models
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• OMNI-SIMPLEMEM: Autoresearch-Guided Discovery of Lifelong Multimodal Agent Memory
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• On-Policy RL Meets Off-Policy Experts: Harmonizing Supervised Fine-Tuning and Reinforcement Learning via Dynamic Weighting
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• Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedback
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• Post-Completion Learning for Language Models
  Current language model training paradigms typically terminate learning upon reaching the end-of-sequence (<eos) token, overlooking the potential learning opportunities in the post-completion space. We…
• Post-Training Large Language Models via Reinforcement Learning from Self-Feedback
  Large Language Models (LLMs) often produce plausible but poorly-calibrated answers, limiting their reliability on reasoning-intensive tasks. We present Reinforcement Learning from Self- Feedback (RLSF…
• ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models
  it remains contentious whether RL truly expands a model’s reasoning capabilities or merely amplifies high-reward outputs already latent in the base model’s distribution, and whether continually scalin…
• R-Zero: Self-Evolving Reasoning LLM from Zero Data
  Self-evolving Large Language Models (LLMs) offer a scalable path toward superintelligence by autonomously generating, refining, and learning from their own experiences. However, existing methods for t…
• RARR: Researching and Revising What Language Models Say, Using Language Models
  Language models (LMs) now excel at many tasks such as question answering, reasoning, and dialog. However, they sometimes generate unsupported or misleading content. A user cannot easily determine whet…
• RL Squeezes, SFT Expands: A Comparative Study of Reasoning LLMs
  Large language models (LLMs) are typically trained by reinforcement learning (RL) with verifiable rewards (RLVR) and supervised fine-tuning (SFT) on reasoning traces to improve their reasoning abiliti…
• RL-STaR: Theoretical Analysis of Reinforcement Learning Frameworks for Self-Taught Reasoner
  The reasoning abilities of large language models (LLMs) have improved with chain-of-thought (CoT) prompting, allowing models to solve complex tasks in a stepwise manner. However, training CoT capabili…
• Recursive Introspection: Teaching Language Model Agents How to Self-Improve
  Even the strongest proprietary large language models (LLMs) do not quite exhibit the ability of continually improving their responses sequentially, even in scenarios where they are explicitly told tha…
• Reflexion: an autonomous agent with dynamic memory and self-reflection
  Recent advancements in decision-making large language model (LLM) agents have demonstrated impressive performance across various benchmarks. However, these state-of-the-art approaches typically necess…
• Rethinking with Retrieval: Faithful Large Language Model Inference
  In this paper, we present a post-processing approach called rethinking with retrieval (RR) for utilizing external knowledge in LLMs. Our method begins by using the chain-of-thought (CoT) prompting met…
• SAND: Boosting LLM Agents with Self-Taught Action Deliberation
  Large Language Model (LLM) agents are commonly tuned with supervised finetuning on ReAct-style expert trajectories or preference optimization over pairwise rollouts. Most of these methods focus on imi…
• Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search
  This typically involves extensive sampling at inference time guided by an external LLM verifier, resulting in a two-player system. Despite external guidance, the effectiveness of this system demonstra…
• Self-Adapting Language Models
  Given a new input, the model produces a self-edit—a generation that may restructure the information in different ways, specify optimization hyperparameters, or invoke tools for data augmentation and g…
• Self-Discover: Large Language Models Self-Compose Reasoning Structures
  *Table 2. All 39 reasoning modules consisting of high-level cognitive heuristics for problem-solving. We adopt them from Fernando et al.* (_2023_). Reasoning Modules 1 How could I devise an experim…
• Self-Evaluation Guided Beam Search for Reasoning
  Breaking down a problem into intermediate steps has demonstrated impressive performance in Large Language Model (LLM) reasoning. However, the growth of the reasoning chain introduces uncertainty and e…
• Self-Improving Model Steering
  Model steering represents a powerful technique that dynamically aligns large language models (LLMs) with human preferences during inference. However, conventional model-steering methods rely heavily o…
• Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges
  Large language models often struggle with length generalization and solving complex problem instances beyond their training distribution. We present a self-improvement approach where models iterativel…
• Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
  “Typical alignment methods include Supervised Fine-Tuning (SFT) (Ouyang et al., 2022; Tunstall et al., 2023a) based on human demonstrations, and Reinforcement Learning from Human Feedback (RLHF) (Chri…
• Self-Questioning Language Models
  Can large language models improve without external data – by generating their own questions and answers? We hypothesize that a pre-trained language model can improve its reasoning skills given only a …
• Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
  Despite their remarkable capabilities, large language models (LLMs) often produce responses containing factual inaccuracies due to their sole reliance on the parametric knowledge they encapsulate. Ret…
• Self-Reasoning Language Models: Unfold Hidden Reasoning Chains with Few Reasoning Catalyst
  In this work, we introduce Self-Reasoning Language Model (SRLM), where the model itself can synthesize longer CoT data and iteratively improve performance through self-training. By incorporating a few…
• Self-Refine: Iterative Refinement with Self-Feedback
  Motivated by how humans refine their written text, we introduce SELF-REFINE, an approach for improving initial outputs from LLMs through iterative feedback and refinement. The main idea is to generate…
• Self-Reflection in LLM Agents: Effects on Problem-Solving Performance
  To improve their performance, we can provide them with a series of cognitive capabilities. For example, we can provide them with a CoT [1–3], access to external memory [22–25], and the ability to lear…
• Self-Taught Evaluators
  Model-based evaluation is at the heart of successful model development – as a reward model for training, and as a replacement for human evaluation. To train such evaluators, the standard approach is t…
• Self-critiquing models for assisting human evaluators
  We fine-tune large language models to write natural language critiques (natural language critical comments) using behavioral cloning. On a topic-based summarization task, critiques written by our mode…
• Self-distillation Enables Continual Learning
  Continual learning, enabling models to acquire new skills and knowledge without degrading existing capabilities, remains a fundamental challenge for foundation models. While on-policy reinforcement le…
• SkillClaw: Let Skills Evolve Collectively with Agentic Evolver
  Large language model (LLM) agents such as OpenClaw rely on reusable skills to perform complex tasks, yet these skills remain largely static after deployment. As a result, similar workflows, tool usage…
• Test-Time Scaling with Reflective Generative Model
  We introduce our first reflective generative model MetaStone-S1, which obtains OpenAI o3- mini’s performance via the new Reflective Generative Form. The new form focuses on highquality reasoning traje…
• The Invisible Leash: Why RLVR May Not Escape Its Origin
  Recent advances in large reasoning models highlight Reinforcement Learning with Verifiable Rewards (RLVR) as a promising method for enhancing AI’s capabilities, particularly in solving complex logical…
• Think Twice Before Trusting: Self-Detection for Large Language Models through Comprehensive Answer Reflection
  Self-detection for Large Language Models (LLMs) seeks to evaluate the trustworthiness of the LLM’s output by leveraging its own capabilities, thereby alleviating the issue of output hallucination. How…
• Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing
  Despite the impressive capabilities of Large Language Models (LLMs) on various tasks, they still struggle with scenarios that involves complex reasoning and planning. Recent work proposed advanced pro…
• Training Language Models to Self-Correct via Reinforcement Learning
  Self-correction is a highly desirable capability of large language models (LLMs), yet it has consistently been found to be largely ineffective in modern LLMs. Current methods for training self-correct…
• Transformer2: Self-adaptive LLMs
  Self-adaptive large language models (LLMs) aim to solve the challenges posed by traditional fine-tuning methods, which are often computationally intensive and static in their ability to handle diverse…
• Truly Self-Improving Agents Require Intrinsic Metacognitive Learning
  Self-improving agents aim to continuously acquire new capabilities with minimal supervision. However, current approaches face two key limitations: their self-improvement processes are often rigid, fai…
• Understanding Before Reasoning: Enhancing Chain-of-Thought with Iterative Summarization Pre-Prompting
  CoT encounters difficulties when key information required for the reasoning process is either implicit or missing. It primarily stems from the fact that CoT emphasizes the stages of reasoning, while n…
• Unsupervised Elicitation of Language Models
  To steer pretrained language models for downstream tasks, today’s post-training paradigm relies on humans to specify desired behaviors. However, for models with superhuman capabilities, it is difficul…
• Voyager: An Open-Ended Embodied Agent with Large Language Models
  We introduce VOYAGER, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human inter…
• When Hindsight is Not 20/20: Testing Limits on Reflective Thinking in Large Language Models
  we set out to clarify these capabilities under a more stringent evaluation setting in which we disallow any kind of external feedback. Our findings under this setting show a split: while self-reflecti…
• ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning
  Our results reveal a significant decline in accuracy as problem complexity grows—a phenomenon we term the “curse of complexity.” This limitation persists even with larger models and increased inferenc…