[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-57b94f28877246db-tandem-reinforcement-learning-aligning-ai-reasonin-summary":3,"summaries-facets-categories":105,"summary-related-57b94f28877246db-tandem-reinforcement-learning-aligning-ai-reasonin-summary":5609},{"id":4,"title":5,"ai":6,"body":13,"categories":75,"created_at":77,"date_modified":77,"description":69,"extension":78,"faq":77,"featured":79,"kicker_label":77,"meta":80,"navigation":87,"path":88,"published_at":89,"question":77,"scraped_at":89,"seo":90,"sitemap":91,"source_id":92,"source_name":93,"source_type":94,"source_url":95,"stem":96,"tags":97,"thumbnail_url":77,"tldr":102,"tweet":77,"unknown_tags":103,"__hash__":104},"summaries\u002Fsummaries\u002F57b94f28877246db-tandem-reinforcement-learning-aligning-ai-reasonin-summary.md","Tandem Reinforcement Learning: Aligning AI Reasoning with Humans",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",6021,530,3221,0.00230025,{"type":14,"value":15,"toc":68},"minimark",[16,21,25,29,32,35,39,42,65],[17,18,20],"h2",{"id":19},"the-problem-rlvr-drift","The Problem: RLVR Drift",[22,23,24],"p",{},"Reinforcement Learning with Verifiable Rewards (RLVR) has enabled large language models to achieve expert-level performance in complex domains like competition math. However, these models often drift toward \"idiosyncratic patterns,\" such as poor readability or language mixing, because the reward signal only cares about the final answer, not the process. This makes the resulting reasoning chains difficult for humans or weaker models to follow.",[17,26,28],{"id":27},"the-solution-tandem-reinforcement-learning-trl","The Solution: Tandem Reinforcement Learning (TRL)",[22,30,31],{},"TRL introduces a collaborative training paradigm to solve this compatibility issue. Instead of training a model in isolation, a \"senior\" model (the one being trained) and a \"frozen junior\" model (a weaker, static model) alternate stochastically to co-generate a single reasoning chain. Both models are rewarded as a team for the final output, and the standard GRPO (Group Relative Policy Optimization) loss is applied to the senior model.",[22,33,34],{},"This forces the senior model to adapt its reasoning style to be legible to the junior model. By requiring the senior to \"hand off\" the reasoning process to a weaker partner, the senior is incentivized to produce chains of thought that are structurally simpler and more logical.",[17,36,38],{"id":37},"key-outcomes","Key Outcomes",[22,40,41],{},"When training the Qwen3-4B-Instruct model on competition math, TRL demonstrated three distinct benefits compared to vanilla GRPO:",[43,44,45,53,59],"ul",{},[46,47,48,52],"li",{},[49,50,51],"strong",{},"Maintained Performance:"," TRL achieved solo reasoning capabilities equal to models trained with standard GRPO.",[46,54,55,58],{},[49,56,57],{},"Increased Legibility:"," The resulting chains of thought were significantly more understandable to the junior model.",[46,60,61,64],{},[49,62,63],{},"Reduced Drift:"," The senior model exhibited less distributional drift, staying closer to standard language patterns rather than devolving into the idiosyncratic, unreadable shorthand often seen in high-performance RLVR models.",[22,66,67],{},"These findings suggest that TRL provides a viable path for creating AI systems that are not only high-performing but also more compatible with human users and other, smaller AI agents.",{"title":69,"searchDepth":70,"depth":70,"links":71},"",2,[72,73,74],{"id":19,"depth":70,"text":20},{"id":27,"depth":70,"text":28},{"id":37,"depth":70,"text":38},[76],"AI & LLMs",null,"md",false,{"content_references":81,"triage":82},[],{"relevance":83,"novelty":84,"quality":84,"actionability":70,"composite":85,"reasoning":86},3,4,3.25,"Category: AI & LLMs. The article discusses a novel approach to reinforcement learning that enhances model reasoning, which is relevant to AI product builders. However, it lacks practical applications or frameworks that the audience can directly implement in their work.",true,"\u002Fsummaries\u002F57b94f28877246db-tandem-reinforcement-learning-aligning-ai-reasonin-summary","2026-06-29 14:30:55",{"title":5,"description":69},{"loc":88},"57b94f28877246db","arXiv cs.AI","article","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.28166","summaries\u002F57b94f28877246db-tandem-reinforcement-learning-aligning-ai-reasonin-summary",[98,99,100,101],"llm","reinforcement-learning","reasoning","ai-alignment","Tandem Reinforcement Learning (TRL) forces stronger models to co-generate reasoning with weaker models, resulting in more legible, robust, and human-compatible chains of thought without sacrificing 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This approach encourages the student model to memorize instance-specific steps, which limits its ability to generalize to novel problems. Instead of learning the underlying logic of how to reason, the model merely learns what to answer for a given input.",[17,5628,5630],{"id":5629},"the-sgpo-framework","The SGPO Framework",[22,5632,5633],{},"Strategy-Guided Policy Optimization (SGPO) shifts the focus from trajectory imitation to strategy distillation. The framework operates through two core mechanisms:",[43,5635,5636,5642],{},[46,5637,5638,5641],{},[49,5639,5640],{},"Strategy Extraction:"," It extracts structured strategy descriptions from strong-model responses. These strategies act as reusable guides for the student model.",[46,5643,5644,5647],{},[49,5645,5646],{},"Comparative Trajectory Construction:"," For each problem, the framework generates both autonomous trajectories (without guidance) and strategy-guided trajectories. This allows the model to directly compare its behavior against a strategic baseline.",[17,5649,5651],{"id":5650},"selective-distillation-and-adaptive-guidance","Selective Distillation and Adaptive Guidance",[22,5653,5654],{},"SGPO addresses the \"how\" and \"when\" of distillation through two technical innovations:",[43,5656,5657,5663],{},[46,5658,5659,5662],{},[49,5660,5661],{},"Token-level Forward-KL Objective:"," This objective selectively transfers the distributional shift caused by strategy conditioning into the unguided policy. By using proximal constraints, it ensures the training remains stable while focusing on the most relevant reasoning signals.",[46,5664,5665,5668],{},[49,5666,5667],{},"Adaptive Instance-level Weighting:"," This mechanism controls the intensity of the guidance. It strengthens the influence of strategies when the model's autonomous exploration fails and automatically reduces that guidance as the model gains competence, preventing over-reliance on the teacher's specific path.",[17,5670,5672],{"id":5671},"performance-gains","Performance Gains",[22,5674,5675],{},"Experiments across four mathematical benchmarks and two model families demonstrate that SGPO consistently outperforms traditional SFT (Supervised Fine-Tuning), on-policy RL, and hybrid-policy baselines. Specifically, the method improved the average score by 2.2 points over the strongest baseline on the Qwen2.5-7B-Instruct model. The results suggest that the forward-KL objective provides a more effective distillation signal than direct trajectory imitation and that strategy distillation scales effectively with the base model's capabilities.",{"title":69,"searchDepth":70,"depth":70,"links":5677},[5678,5679,5680,5681],{"id":5622,"depth":70,"text":5623},{"id":5629,"depth":70,"text":5630},{"id":5650,"depth":70,"text":5651},{"id":5671,"depth":70,"text":5672},[76],{"content_references":5684,"triage":5685},[],{"relevance":83,"novelty":84,"quality":84,"actionability":70,"composite":85,"reasoning":5686},"Category: AI & LLMs. The article discusses a novel framework (SGPO) for improving LLM reasoning, which addresses a specific pain point in AI model training. However, it lacks practical steps or frameworks that the audience can directly implement in their product development.","\u002Fsummaries\u002Faebe3858a6ac8196-strategy-guided-policy-optimization-for-llm-reason-summary","2026-06-24 12:56:40",{"title":5612,"description":69},{"loc":5687},"aebe3858a6ac8196","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.24064","summaries\u002Faebe3858a6ac8196-strategy-guided-policy-optimization-for-llm-reason-summary",[98,5695,100,5696],"machine-learning","policy-optimization","Strategy-Guided Policy Optimization (SGPO) improves LLM reasoning by distilling reusable problem-solving strategies rather than just imitating specific solution trajectories, leading to better generalization.",[100,5696],"xY7sTVcYfmWl4hn7fpYjJ1ciMy9Y2BMG21hVTWuUWEY",{"id":5701,"title":5702,"ai":5703,"body":5708,"categories":5808,"created_at":77,"date_modified":77,"description":69,"extension":78,"faq":77,"featured":79,"kicker_label":77,"meta":5809,"navigation":87,"path":5824,"published_at":5825,"question":77,"scraped_at":5826,"seo":5827,"sitemap":5828,"source_id":5829,"source_name":5830,"source_type":94,"source_url":5831,"stem":5832,"tags":5833,"thumbnail_url":77,"tldr":5836,"tweet":77,"unknown_tags":5837,"__hash__":5838},"summaries\u002Fsummaries\u002Fe49f16bf5dbabedc-fixing-grpo-failure-modes-in-production-summary.md","Fixing GRPO Failure Modes in Production",{"provider":7,"model":8,"input_tokens":5704,"output_tokens":5705,"processing_time_ms":5706,"cost_usd":5707},6684,817,4693,0.0028965,{"type":14,"value":5709,"toc":5803},[5710,5714,5717,5737,5741,5744,5770,5774,5777],[17,5711,5713],{"id":5712},"the-structural-weaknesses-of-grpo","The Structural Weaknesses of GRPO",[22,5715,5716],{},"GRPO (Group Relative Policy Optimization) is widely favored for its efficiency, as it eliminates the need for a critic network. However, its reliance on group-relative advantage normalization creates three primary failure modes that stall training:",[43,5718,5719,5725,5731],{},[46,5720,5721,5724],{},[49,5722,5723],{},"Advantage Collapse:"," Occurs when all sampled responses in a group receive the same reward (e.g., all correct or all incorrect). This results in near-zero advantage, effectively killing the gradient signal. This is most common on very hard or very easy prompts.",[46,5726,5727,5730],{},[49,5728,5729],{},"Entropy Collapse:"," As the model converges, it may lose generation diversity. Once entropy drops below a critical threshold (typically \u003C 0.5 nats), the model becomes stuck in a narrow mode, making it difficult to recover without external intervention.",[46,5732,5733,5736],{},[49,5734,5735],{},"KL Drift:"," Using a blunt KL penalty coefficient often forces the model to choose between reward hacking (low penalty) or stagnation (high penalty). Baking KL into the reward signal further distorts the advantage normalization process.",[17,5738,5740],{"id":5739},"engineering-solutions-via-dapo","Engineering Solutions via DAPO",[22,5742,5743],{},"The DAPO (Dynamic Sampling Policy Optimization) framework provides specific algorithmic fixes to these issues:",[43,5745,5746,5752,5758,5764],{},[46,5747,5748,5751],{},[49,5749,5750],{},"Dynamic Sampling:"," Instead of training on all groups, filter out groups with zero reward variance. This prevents the model from updating on noise.",[46,5753,5754,5757],{},[49,5755,5756],{},"Asymmetric KL Clipping:"," By using a higher upper bound for the probability ratio, the model can aggressively reinforce correct responses without needing to compress its entire output distribution, which helps preserve entropy.",[46,5759,5760,5763],{},[49,5761,5762],{},"Decoupled KL:"," Remove the KL penalty from the reward signal entirely. Apply it as a direct loss term after advantage computation to prevent reward distortion.",[46,5765,5766,5769],{},[49,5767,5768],{},"Token-Level Normalization:"," Standard GRPO normalizes at the sample level, which biases the model against long chain-of-thought reasoning. Normalizing by total token count ensures that longer, more complex reasoning traces are weighted appropriately.",[17,5771,5773],{"id":5772},"production-best-practices","Production Best Practices",[22,5775,5776],{},"Beyond the algorithm, the success of GRPO depends on the quality of the reward signal and the training pipeline.",[43,5778,5779,5785,5791,5797],{},[46,5780,5781,5784],{},[49,5782,5783],{},"Audit the Reward Model:"," If the verifier is noisy, it will inject false signals that exacerbate advantage collapse.",[46,5786,5787,5790],{},[49,5788,5789],{},"Monitor Entropy:"," Track per-token entropy as a first-class metric. If it stays below 0.5 nats for more than 50 steps, the model is likely collapsing.",[46,5792,5793,5796],{},[49,5794,5795],{},"Manage SFT Bias:"," If the initial SFT checkpoint is already over-fitted to a specific format, it will be more prone to entropy collapse during RL.",[46,5798,5799,5802],{},[49,5800,5801],{},"Hyperparameter Tuning:"," While increasing group size (G) can stabilize estimates, it is often more compute-efficient to use dynamic sampling to discard low-variance groups than to simply increase the number of rollouts.",{"title":69,"searchDepth":70,"depth":70,"links":5804},[5805,5806,5807],{"id":5712,"depth":70,"text":5713},{"id":5739,"depth":70,"text":5740},{"id":5772,"depth":70,"text":5773},[76],{"content_references":5810,"triage":5820},[5811,5816],{"type":5812,"title":5813,"author":5814,"context":5815},"paper","DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","DeepSeek-AI","mentioned",{"type":5812,"title":5817,"author":5818,"context":5819},"DAPO: Dynamic Sampling Policy Optimization","Yu et al.","recommended",{"relevance":5821,"novelty":84,"quality":84,"actionability":84,"composite":5822,"reasoning":5823},5,4.35,"Category: AI & LLMs. The article provides in-depth insights into the failure modes of GRPO and actionable solutions through DAPO techniques, addressing a specific pain point for AI developers working on production models. The detailed explanation of dynamic sampling and KL clipping offers practical steps that can be implemented in AI training workflows.","\u002Fsummaries\u002Fe49f16bf5dbabedc-fixing-grpo-failure-modes-in-production-summary","2026-06-22 17:19:40","2026-06-23 12:56:49",{"title":5702,"description":69},{"loc":5824},"e49f16bf5dbabedc","Level Up Coding","https:\u002F\u002Flevelup.gitconnected.com\u002Fgrpo-in-production-the-failure-modes-nobody-writes-about-5d59c3fc9c3b?source=rss----5517fd7b58a6---4","summaries\u002Fe49f16bf5dbabedc-fixing-grpo-failure-modes-in-production-summary",[98,5834,5835,99],"agents","ai-tools","GRPO is more efficient than PPO but prone to silent failures like advantage collapse and entropy loss. Using Dynamic Sampling Policy Optimization (DAPO) techniques—specifically dynamic sampling, token-level normalization, and decoupled KL—is essential for stable production training.",[99],"4CEhNHYzoOyN8sKq7uCvHQxt3W7rlJzDp-bzL0gTCkE",{"id":5840,"title":5841,"ai":5842,"body":5847,"categories":5910,"created_at":77,"date_modified":77,"description":69,"extension":78,"faq":77,"featured":79,"kicker_label":77,"meta":5911,"navigation":87,"path":5926,"published_at":5927,"question":77,"scraped_at":5927,"seo":5928,"sitemap":5929,"source_id":5930,"source_name":5931,"source_type":94,"source_url":5932,"stem":5933,"tags":5934,"thumbnail_url":77,"tldr":5936,"tweet":77,"unknown_tags":5937,"__hash__":5938},"summaries\u002Fsummaries\u002F97a07fcdff0767b4-vibethinker-3b-high-performance-reasoning-at-3b-pa-summary.md","VibeThinker-3B: High-Performance Reasoning at 3B Parameters",{"provider":7,"model":8,"input_tokens":5843,"output_tokens":5844,"processing_time_ms":5845,"cost_usd":5846},7351,762,3720,0.00298075,{"type":14,"value":5848,"toc":5905},[5849,5853,5856,5860,5863,5883,5887,5890],[17,5850,5852],{"id":5851},"the-efficiency-of-specialization","The Efficiency of Specialization",[22,5854,5855],{},"VibeThinker-3B, developed by Sina Weibo Inc, challenges the trend of scaling model size to improve reasoning. By focusing exclusively on verifiable tasks like mathematics, coding, and STEM, this 3-billion-parameter model matches the performance of models hundreds of times its size (such as DeepSeek V3.2 or Kimi K2.5) on benchmarks like AIME26. It is designed as a specialist tool; the authors recommend using larger general-purpose models for open-domain knowledge tasks.",[17,5857,5859],{"id":5858},"the-spectrum-to-signal-pipeline","The Spectrum-to-Signal Pipeline",[22,5861,5862],{},"The model's performance is driven by a four-stage post-training pipeline built on the Qwen2.5-Coder-3B base:",[43,5864,5865,5871,5877],{},[46,5866,5867,5870],{},[49,5868,5869],{},"Curriculum SFT:"," A two-stage supervised fine-tuning process that builds a broad 'spectrum' of reasoning paths, using diversity-exploring distillation to maintain multiple valid solution trajectories.",[46,5872,5873,5876],{},[49,5874,5875],{},"Multi-domain Reasoning RL:"," Uses MaxEnt-Guided Policy Optimization (MGPO) to train across math, code, and STEM. Notably, it abandons progressive context expansion in favor of a consistent 64K long-context window. A 'Long2Short' math stage further optimizes performance by rewarding shorter, correct reasoning chains to reduce token redundancy.",[46,5878,5879,5882],{},[49,5880,5881],{},"Self-Distillation & Instruct RL:"," Offline distillation merges RL checkpoints into a single student model, followed by instruction-tuning to ensure the model maintains controllability without sacrificing reasoning depth.",[17,5884,5886],{"id":5885},"test-time-scaling-with-clr","Test-Time Scaling with CLR",[22,5888,5889],{},"To further boost performance without increasing parameter count, the researchers implemented Claim-Level Reliability Assessment (CLR). This test-time scaling method involves:",[5891,5892,5893,5896,5899,5902],"ol",{},[46,5894,5895],{},"Generating 32 trajectories per problem.",[46,5897,5898],{},"Extracting decision-relevant claims from each.",[46,5900,5901],{},"Using the model as its own verifier to assign binary verdicts to each claim.",[46,5903,5904],{},"Weighting the final answer based on the reliability of the claims within the reasoning path. This method significantly lifts scores on benchmarks like AIME26 (from 94.3 to 97.1).",{"title":69,"searchDepth":70,"depth":70,"links":5906},[5907,5908,5909],{"id":5851,"depth":70,"text":5852},{"id":5858,"depth":70,"text":5859},{"id":5885,"depth":70,"text":5886},[76],{"content_references":5912,"triage":5923},[5913,5916,5920],{"type":5812,"title":5914,"url":5915,"context":5819},"VibeThinker-3B: A 3B Dense Reasoning Model Built on Qwen2.5-Coder-3B With the Spectrum-to-Signal Post-Training Pipeline","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2606.16140v1",{"type":5917,"title":5918,"url":5919,"context":5819},"tool","vLLM","https:\u002F\u002Fgithub.com\u002Fvllm-project\u002Fvllm",{"type":5917,"title":5921,"url":5922,"context":5819},"SGLang","https:\u002F\u002Fgithub.com\u002Fsgl-project\u002Fsglang",{"relevance":83,"novelty":83,"quality":84,"actionability":70,"composite":5924,"reasoning":5925},3.05,"Category: AI & LLMs. The article discusses a new AI model, VibeThinker-3B, which is relevant to the AI & LLMs category, but it primarily focuses on technical specifications and performance metrics rather than practical applications for product builders. While it presents some novel insights into model efficiency and reasoning techniques, it lacks actionable steps that developers can implement in their own projects.","\u002Fsummaries\u002F97a07fcdff0767b4-vibethinker-3b-high-performance-reasoning-at-3b-pa-summary","2026-06-20 12:56:43",{"title":5841,"description":69},{"loc":5926},"97a07fcdff0767b4","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F06\u002F19\u002Fvibethinker-3b-a-3b-dense-reasoning-model-built-on-qwen2-5-coder-3b-with-the-spectrum-to-signal-post-training-pipeline\u002F","summaries\u002F97a07fcdff0767b4-vibethinker-3b-high-performance-reasoning-at-3b-pa-summary",[98,5835,5935,100],"coding","VibeThinker-3B is a compact, open-source reasoning model that achieves performance comparable to massive models on math and coding tasks by using a specialized 'Spectrum-to-Signal' post-training pipeline.",[100],"7AxYcDegiR90oeD9i3t7iSvLWLk01K6hw5OiKzjtV4E",{"id":5940,"title":5941,"ai":5942,"body":5947,"categories":5975,"created_at":77,"date_modified":77,"description":69,"extension":78,"faq":77,"featured":79,"kicker_label":77,"meta":5976,"navigation":87,"path":5985,"published_at":5986,"question":77,"scraped_at":5986,"seo":5987,"sitemap":5988,"source_id":5989,"source_name":93,"source_type":94,"source_url":5981,"stem":5990,"tags":5991,"thumbnail_url":77,"tldr":5993,"tweet":77,"unknown_tags":5994,"__hash__":5995},"summaries\u002Fsummaries\u002F778bfd017115b992-svot-enhancing-spatial-reasoning-via-state-aware-v-summary.md","SVoT: Enhancing Spatial Reasoning via State-Aware Visualization",{"provider":7,"model":8,"input_tokens":5943,"output_tokens":5944,"processing_time_ms":5945,"cost_usd":5946},4061,492,2998,0.00175325,{"type":14,"value":5948,"toc":5970},[5949,5953,5956,5960,5963,5967],[17,5950,5952],{"id":5951},"the-limitation-of-text-only-reasoning","The Limitation of Text-Only Reasoning",[22,5954,5955],{},"Standard Chain-of-Thought (CoT) prompting often struggles with spatial reasoning because text is inherently linear and lacks the structural grounding required to represent 2D or 3D environments. When models rely solely on linguistic tokens to track spatial coordinates or object relationships, they frequently suffer from 'drift' or logical inconsistencies as the sequence length increases.",[17,5957,5959],{"id":5958},"state-aware-visualization-of-thought-svot","State-Aware Visualization-of-Thought (SVoT)",[22,5961,5962],{},"SVoT introduces a novel framework that bridges the gap between linguistic reasoning and spatial awareness. Instead of forcing the model to describe spatial states purely through text, SVoT forces the model to generate a 'visualized' state representation at each step of the reasoning process. This approach treats the spatial layout as a dynamic state that must be updated and verified throughout the inference chain.",[17,5964,5966],{"id":5965},"reinforcement-learning-for-spatial-accuracy","Reinforcement Learning for Spatial Accuracy",[22,5968,5969],{},"The core innovation of SVoT is the use of Reinforcement Learning (RL) to train the model to produce these visual state representations. By rewarding the model for maintaining spatial consistency and logical accuracy across its 'thought' steps, the system learns to ground its reasoning in a structured spatial map. This prevents the model from hallucinating object positions or invalid movements, as the visual state acts as a persistent memory buffer that the model must reconcile with its next textual action. The result is a significant reduction in spatial errors compared to traditional CoT methods, as the model is effectively forced to 'see' the environment it is reasoning about.",{"title":69,"searchDepth":70,"depth":70,"links":5971},[5972,5973,5974],{"id":5951,"depth":70,"text":5952},{"id":5958,"depth":70,"text":5959},{"id":5965,"depth":70,"text":5966},[76],{"content_references":5977,"triage":5983},[5978],{"type":5812,"title":5979,"author":5980,"url":5981,"context":5982},"SVoT: State-aware Visualization-of-Thought for Spatial Reasoning via Reinforcement Learning","Unknown","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.11770","cited",{"relevance":83,"novelty":84,"quality":84,"actionability":70,"composite":85,"reasoning":5984},"Category: AI & LLMs. The article discusses a novel framework (SVoT) that enhances spatial reasoning in LLMs, addressing a specific limitation of text-only reasoning. However, while it presents new insights into the use of reinforcement learning for spatial accuracy, it lacks practical steps for implementation that the audience could directly act upon.","\u002Fsummaries\u002F778bfd017115b992-svot-enhancing-spatial-reasoning-via-state-aware-v-summary","2026-06-11 12:57:20",{"title":5941,"description":69},{"loc":5985},"778bfd017115b992","summaries\u002F778bfd017115b992-svot-enhancing-spatial-reasoning-via-state-aware-v-summary",[98,5695,5992,99],"spatial-reasoning","SVoT improves spatial reasoning in LLMs by using reinforcement learning to generate state-aware visual representations of thought, allowing models to track complex spatial relationships more accurately than text-only chain-of-thought.",[5992,99],"4I2cTchJrNX04sbDTy_vPmMoVe19ZsfQNXrOGy6HZU4"]