Sources

Native Action-Prior Learning from Videos for World Action Models huggingface.co

World action models integrate future visual dynamics with robot action prediction, but their scalability remains limited by the need for action-annotated robot trajectories. Observation-only videos contain rich evidence about interaction dynamics, but existing approaches typically use them either to pretrain visual representations that must later be adapted for control, or to infer latent actions that are subsequently grounded to robot commands. We present NAVA-WAM, which introduces native actio

World Action Modeling with Progressive Visual Planning huggingface.co

World action models (WAMs) have emerged as a promising paradigm for robotic control by jointly predicting future visual dynamics and actions from an initial observation and instruction. However, existing WAMs struggle with long-horizon prediction, as generating dense video rollouts is highly inefficient. Some recent WAMs address this by predicting a single future frame without generating the full video, but this approach neglects how to progress toward the goal. We present ProWAM, a progressive

PointWAM: 3D World Action Modeling for Dexterous Robotic Manipulation huggingface.co

World action models jointly learn to forecast world dynamics and predict robot actions, such that the learned internal world dynamics guide accurate actions. Existing approaches typically represent the world as RGB frames or latent counterparts while predicting actions as end-effector poses or joint angles, but they often struggle to capture the 3D spatial structure and contact geometry central to dexterous manipulation. We introduce Point World Action Model (PointWAM), a 3D world action model t

Beyond Future Prediction: Denoising as Generative Adaptation for Robot Control huggingface.co

Pretrained generative Diffusion Transformers (DiTs) capture rich pixel-level visual and language-conditioned structure through large-scale image and video generation training. A growing line of robot policies builds on this generative prior, but how it should be transferred to control remains unclear, and existing approaches commonly instantiate this transfer through future visual prediction. We ask a more basic question: what a pretrained generative DiT actually contributes to action learning,

Efficient Reasoning Training Does Not Always Harm CoT Faithfulness and Monitorability huggingface.co

Chain-of-thought (CoT) reasoning allows humans to inspect how large language models reach their answers, and oversee model behaviour. This reasoning comes at an increased inference cost, motivating efficient methods that train models to solve tasks using fewer tokens. However, a common concern is that such training may cause models to skip important reasoning steps, so the CoT no longer faithfully reflects the model’s decision. It is unclear whether or when this occurs in practice, since differe

From Gradients to Capabilities: Understanding Multi-Teacher On-Policy Distillation huggingface.co

Multi-teacher on-policy distillation (MOPD) aims to combine the strengths of RL-trained teachers in a single student, but how teacher signals affect parameter changes remains underexplored. We study Qwen3-1.7B with four domain teachers trained with RL from the same initialization as the student, comparing gradients, optimizer updates, and task learning curves, with additional SmolLM3-3B diagnostics. We find that several factors influence teacher signals. First, loss averaging implicitly weights

Latent-MOPD: Latent Multi-Teacher On-Policy Distillation huggingface.co

On-policy distillation (OPD) trains a student on the responses it generates. Existing LLM multi-teacher OPD transfers what specialists predict through their output distributions. We introduce Latent-MOPD, to our knowledge the first representation-level multi-teacher OPD method for LLMs. It integrates existing specialists through both their predictions and the hidden states used to compute them, without additional teacher training. To coordinate representation supervision from multiple specialist

Covert Assistance: Helpful LLM Agents Evade Oversight in Multi-Agent Systems huggingface.co

Multi-agent safety research shows helpful agents can quietly cross oversight boundaries even without incentives to deceive. In a simulated software workflow where a planner hires an external developer, benign agents routinely communicated in ways that circumvented monitors, suggesting covert coordination is a baseline behavior rather than an adversarial edge case.

Language Models that Play Chess and Explain Their Moves huggingface.co

Queen pairs Grandmaster-strength play with natural-language explanations of its plans, closing the gap between silent engines and chatty but weak LMs. The 4B-parameter model uses a framework for domain-specific reasoning and ships with code and a project page for reproduction.

Multilingual GSM-Symbolic: What determines capability transfer across languages? huggingface.co

A new symbolic variant of GSM extends math reasoning evaluation across languages to identify which factors predict transfer. The benchmark aims to spare developers exhaustive language-pair testing and pinpoint bottlenecks limiting low-resource performance, replacing saturation-prone datasets with controllable, comparable problems.

Source Preference in the Wild: How LLM Agents Favor Items by Source, and How to Reduce It huggingface.co

Tested across three domains with identical items placed identically, 12 agent models each favored certain sources and avoided others when picking products, hotels, or citations. The study quantifies this source preference in end-to-end search and proposes mitigations to reduce skewed selections on users’ behalf.

Does Learning Protein Folding Generalize to Broader Reasoning? huggingface.co

Fold2Reason post-trains models on FoldingCorpus, a QA dataset derived from solved protein structures where each example yields checkable spatial and topological statements. The recipe tests whether structural logic from folding generalizes, and finds the training produces reusable reasoning gains beyond biology.

Science or Slop?: Benchmarking and Mitigating Scientific Slop in AI-Generated Papers huggingface.co

AI-generated papers often read plausibly section by section while the scientific logic linking them collapses, evading token-based detectors. The benchmark scores six failure modes across structure, argument, and other dimensions, then proposes mitigations to flag papers whose parts look fine but whose reasoning does not cohere.

Looping Beyond Twice: A Scalable Recipe for Looped Mixture-of-Experts huggingface.co

Prior looped Transformers hit diminishing returns after a couple of iterations, wasting FLOPs on added depth. LOOM is a recipe that makes recurrent depth pay off for large MoE models under FLOPs-matched comparisons, extending looping as a viable scaling axis alongside parameters and width.

RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversations huggingface.co

A companion that talks with a person for months should come to understand them. It should remember what they said, infer who they are, and know when the past bears on the message in front of it. Testing this requires a real person’s record, and such records are private, so benchmarks generate the person and the questions and settle in advance what matters. We release \bench, ten real relationships with an AI companion: 27,218 messages over up to 120 days, released as the conversation and four fi

Triadic Linear Attention: Three-Dimensional Recurrent States for Long-Context Sequence Modeling huggingface.co

Recurrent neural networks (RNNs) compress the historical context into a memory state of fixed size, thus allowing for constant-time inference. The memory state size is a crucial factor in their performance, as exemplified by the strong performance and resurgence of linear attention, which extends the vector-valued hidden states of ordinary RNNs to matrix-valued hidden states. Crucially, linear attention does so in a parameter-efficient way, in particular by using an outer product of the key and

On-Policy Parameter Update Direction Underlies Generalization in LLM Post-Training huggingface.co

The strong generalization performance of on-policy post-training paradigms has motivated studies of their parameter update behaviors. However, these studies treat the observed behaviors only as byproducts in on-policy training, overlooking their potential to serve as optimization principles for improving the generalization of other paradigms such as supervised fine-tuning (SFT). To address this limitation, we investigate whether there exists a specific on-policy update behavior that can achieve

FrugalEvo: Towards Cost-Aware LLM-Guided Program Evolution huggingface.co

LLM-guided evolutionary methods, such as AlphaEvolve, have emerged as powerful approaches for challenging computational optimization problems, such as circle packing. However, prior work typically optimizes performance gain over a fixed number of iterations. We argue that practical optimization should maximize gain per unit cost. To this end, we propose FrugalEvo, a cost-aware evolutionary framework where a stronger, higher-cost LLM explores solution strategies, and a cheaper LLM implements them

HyperBrowseComp: A Multilingual and Multimodal Stress Test for Web-Browsing Agents huggingface.co

We introduce HyperBrowseComp, a multilingual and multimodal browsing benchmark comprising 423 manually authored and human-validated questions across 13 languages, written by native or highly proficient speakers. Questions are designed to be extremely challenging. Each question targets a concise, publicly verifiable answer whose discovery requires locating obscure evidence, following multi-step clue chains, or inspecting heterogeneous sources such as videos, scanned documents, images, or maps. Ea

VeriHarness: Scaling Agentic Verification for Long-Horizon Tasks huggingface.co

As LLM agents undertake increasingly complex, long-horizon tasks, verifying their outputs becomes increasingly challenging. We study how verification capability can be strengthened with a fixed base model, without access to reference answers or grading rubrics at test time. Repeated sampling yields multiple rollouts that can contain complementary correct claims, but we need a reliable verification mechanism to determine which claims to trust. We first find that disagreement often exposes correct

Octrees as an Explicit 3D Language huggingface.co

Existing 3D large language models (LLMs) compromise on two fronts: they compress shapes into latent codebook indices or coordinate text, which removes spatial structure from what the model observes, and they acquire the 3D modality by fine-tuning the backbone, which overwrites its general language ability. We present OctLLM, which addresses both limitations. Geometry enters as an explicit 3D sequence of octree occupancy tokens. However, full octree sequences grow rapidly with depth; OctLLM there

Local Support Learning huggingface.co

We explore catastrophic forgetting in the context of large pre-trained models. By considering forgetting as a geometric problem in the input space of each weight matrix, we uncover a natural retention objective under which updates produced by gradient-based optimizers are suboptimal. Following this observation, we propose Local Support Learning (LSL), a general-purpose framework that augments gradient-based training for retention of prior capabilities without access to prior data. During a new l

Pivot-SD: Efficient Self-Distillation for Masked Diffusion Language Models huggingface.co

Masked diffusion language models (dLMs) offer a promising parallel alternative to autoregressive models for complex reasoning. However, they face a distinct credit-assignment challenge, since a few commitments during denoising sharply reduce the uncertainty over the remaining masked positions and shape much of the response. Most post-training recipes for dLMs do not use this signal to decide which tokens to train on: they typically train on the final text or assign rewards to whole denoising ste

Equal Ranking Quality, Different Decisions: Measuring and Reducing Order Dependence in LLM Scorers huggingface.co

In passage reranking, response ranking and multi-document question answering, LLMs can score several candidate documents or responses together in one prompt, each still receiving its own score. Such scorers are selected on ranking quality, but their scores determine a decision: what a score threshold retains, a reader answers, or which chosen/rejected pair enters preference training. Because the candidates share that prompt, reordering them changes their scores. The same query over the same cand

QuantWM: Temporally Consistent 2-Bit KV Cache Quantization for Video World Models huggingface.co

Video world models achieve long-range temporal consistency by storing KV cache during generation, but the growing cache makes KV cache memory a major deployment bottleneck, which motivates low-bit quantization study for efficiency. Existing 2-bit KV cache quantization methods can achieve nearly lossless performance on VBench, however, when applied to video world models, we find they still cause severe temporal flickering and visual degradation. Meanwhile, deeper investigates show that Key quanti

WEFT: Scaling Tool-Use Post-Training for General-Purpose Agents huggingface.co

Recent efforts to scale tool-use post-training have largely centered on the synthesis of executable environments, which constitute only one component of a broader agentic interaction system comprising the environment, task, agent harness, and evaluator. Scaling environments in isolation, however, does not guarantee commensurate gains in model performance, because reliable learning signals depend on coherent interactions among all components of the agentic interaction system. To address this prob

Rollout-Marginal Distillation for Long-Horizon Autoregressive Video Generation huggingface.co

Autoregressive (AR) video diffusion enables low-latency, streamable video generation, but prediction errors often accumulate over long rollouts. Training the generator on its own rollouts exposes it to these imperfect histories. However, existing video-level distribution matching distillation (DMD) scores the whole rollout jointly. Because a chunk is evaluated together with its past and future, its correction can favor matching artifacts in the surrounding context merely to preserve temporal con

HelixWorld: A Real-time Interactive Audio-Visual World Model huggingface.co

World simulation is inherently multisensory, demanding synchronized visual and acoustic dynamics in real time. Yet prevailing interactive world models remain strictly silent, focusing exclusively on visual rendering and control while overlooking the acoustic dimension. We present HelixWorld, a real-time interactive audio-visual world model where visual scenes and camera-grounded spatial stereo sound co-evolve natively under user interaction. We curate a high-fidelity spatial audio-visual dataset

ProAR: Learning Prospective Reasoning with Autoregressive Video Models huggingface.co

Autoregressive (AR) video models excel at causal generation, but their reliance on next-chunk prediction confines them to a short-sighted, reactive paradigm. This limitation is particularly consequential for reasoning-oriented generation, where achieving a target outcome through valid intermediate states matters more than local visual plausibility. To address this challenge, we propose Learning Prospective Reasoning with Autoregressive Video Models (ProAR), a novel framework that transforms auto

PDE-JEPA: Predictive Representation Learning of Latent Dynamics Modeling for Parametric PDEs huggingface.co

Physical trajectories contain more than snapshots of a system: they also reveal how its states evolve under governing conditions. However, representation learning for parametric partial differential equations (PDEs) has largely relied on reconstruction-based objectives that emphasize recovering observed physical fields. In this paper, we investigate predictive representation pretraining as an alternative to reconstruction-based learning. We find that predictive representations preserve rich phys

World Embedding Benchmark huggingface.co

Physical fidelity has received increasing attention in world models and video generation, yet how video representations encode physical information remains less understood. We introduce the World Embedding Benchmark, comprising 8,000 controlled simulation cases from 80 families spanning fluid mechanics, solid mechanics, dynamics, and optics & electromagnetism. Each case pairs a rendered video with simulation-derived physical annotations, supporting three complementary tasks: text-video retrieval

Spatial Memory Intelligence: Endowing World Models with Understanding-Driven Long-Term Memory huggingface.co

Long-video generation and world models have shown strong potential for interactive entertainment and embodied simulation by predicting future observations conditioned on user actions and historical memory. However, as memory sequences grow longer and their structures become increasingly complex, managing long-range spatial context becomes increasingly challenging, calling for a more intelligent and systematic memory-management strategy. Building on the advancing spatial reasoning capabilities of

FrameMorrow: Future-guided Frame Selection with Prospective Tokens for Long-Horizon Video Generation huggingface.co

Long-horizon video generation requires models to effectively leverage an increasingly long generation history. As the generated history grows, retaining all previous content becomes increasingly expensive and redundant, making effective historical selection essential. Existing approaches often determine historical relevance based on the current content. However, information relevant to the present is not necessarily useful for future generation, while seemingly less relevant history may become i

Learning from Runtime Feedback through Failure-Bank Self-Evolution for Vision-Language-Action Models huggingface.co

Vision-language-action (VLA) models generalize broadly across robotic manipulation tasks, but complex environments require balancing task success with unintended contact. Runtime shields can correct individual actions, but they leave the underlying policy unchanged, so repeated disagreements may create a persistent policy-shield mismatch that blocks task progress. To address this challenge, we introduce FailBank, a four-stage self-evolving framework that converts runtime feedback into persistent

Skill2Real: Agentic Skill Learning for Zero-Shot Sim-to-Real Robot Manipulation huggingface.co

Transferring robotic skills from simulation to reality requires task knowledge that remains usable across differences in perception, dynamics, and embodiment. We introduce Skill2Real, an agentic policy framework that learns executable skills through a shared application programming interface (API). A Proposer-Verifier-Governor (PVG) loop uses privileged simulation evidence to diagnose outcomes and validate updates, while keeping learned skills grounded in public observations and API semantics. T

MotorMind: Scaffolding General Vision Language Models for Zero-Shot Robot Manipulation huggingface.co

Vision-language-action (VLA) models have advanced robotic manipulation, but their zero-shot generalization in new tasks and environments remains limited, and their reliance on specialized training keeps them from benefiting directly from rapidly advancing general-purpose vision-language models (VLMs). In parallel, recent agentic robotic systems leverage VLMs for high-level reasoning or coding agents for robot control, but often depend on extensive external models and tools, introducing additiona

Scaling Trajectories for Complex Tasks through Recursive Self-Rewrite huggingface.co

Successful trajectories on difficult tasks provide valuable supervision for model improvement, but specialized harnesses introduce interventions that may be unavailable during deployment. We propose Recursive Self-Rewrite (RSR), a framework that uses one base model, Qwen-3.8-27B, to discover successful solutions under diverse harnesses and reconstruct them as training trajectories under a general harness. A planner extracts procedures into runbooks, a critic screens for verifier and solution lea

MetaRubric: Learning to Reward for Rubric-Based Reinforcement Learning huggingface.co

Rubric-based reinforcement learning extends reward-driven optimization to open-ended tasks by assigning partial credit to individual response requirements. However, rubric judges can assign a high criterion score even when the information or action it requires is absent from the response, a failure mode we term Vacuous Credit. Such awards persist after the required information is removed and can reverse the sign of a response’s GRPO advantage. To address this problem, we introduce MetaRubric, wh

Where Does Retrieval-Based Open-Ended Evaluation Fail? Automatic Taxonomy Induction from Long-Form Medical Answer Factuality Verification huggingface.co

Retrieval-based factuality evaluation, where LLM-generated claims are verified against evidence from authoritative medical corpora, has become the dominant paradigm for scalable hallucination detection in high-stakes clinical settings. Despite the urgency of reliable and transparent medical fact verification, most systems measure performance with aggregate metrics like F1, which obscure where and why failures occur. Existing RAG diagnostics require gold answers or annotated gold evidence, neithe

4DCodeBench: Benchmarking Agents on Inverse Graphics of Dynamic Scenes huggingface.co

We introduce 4DCodeBench, a benchmark for 4D inverse graphics through code generation, in which agents reconstruct dynamic scenes from video as executable graphics programs. To accomplish this, agents must translate visual observations into compact representations of scene structure and dynamics, by implementing abstractions such as physical simulations to reproduce complex behavior. To evaluate this capability, we curate a set of real-world videos and construct synthetic scenes spanning diverse

Science Utopia? Closed-Loop LLM Simulation of Academic Research Ecosystems huggingface.co

Scientific progress emerges from a longitudinal ecosystem in which researchers, institutions, funding agencies, collaboration networks, and the scientific literature co-evolve. As AI becomes increasingly involved throughout the scientific research cycle, understanding these interconnected and evolving processes becomes increasingly important. We introduce SciUtopia, a persistent, closed-loop LLM-agent simulation framework for studying academic research ecosystems. SciUtopia models interconnected

MinkowskiPE: Minkowski Positional Encoding for Spatiotemporal Perception huggingface.co

Modeling spatiotemporal coupling is a key challenge in building physical intelligence across scales, from microscopic to macroscopic. Existing models capture such structure broadly through physics-motivated dynamical formulations or learning-motivated architectures. The former provide stronger priors but may constrain flexibility, whereas the latter are more flexible but leave the spatiotemporal coupling largely implicit. We therefore seek an approach that combines flexible learning with an expl

Collective Bias Mitigation via Model Routing and Collaboration huggingface.co

Large language models (LLMs) are increasingly deployed in public health, finance, and governance, requiring both accuracy and societal value alignment. Despite recent advances, LLMs often perpetuate or amplify bias embedded in their training data, posing challenges to fairness. While self-debiasing encourages an LLM to identify and correct its own biases, relying on a single model’s intrinsic knowledge may be insufficient to address deeply ingrained stereotypes. To address this limitation, we in

Tail-Influence Sampling for CVaR Policy Evaluation huggingface.co

Policies with similar mean returns can differ sharply in rare failures, yet estimating lower-tail conditional value-at-risk (CVaR) accurately can require many costly rollouts. When different conditional components of a stochastic workflow can be queried separately, we ask how to allocate a fixed evaluation budget to estimate a fixed policy’s CVaR most accurately. We derive a tail influence for each queryable conditional law that aggregates how its uncertainty affects CVaR across every Bellman re

LVMT: Video Mask Transformer for Long-term Video Segmentation huggingface.co

Existing online video segmentation methods struggle to track objects in long, complex videos with long-term occlusions. We hypothesize that this limitation is caused by (i) the inability of their temporal propagation mechanism to adaptively select the object information that is propagated across time, and (ii) their inability to be trained on long videos due to memory requirements and vanishing gradients. To address the first limitation, we propose to use a lightweight GRU-based temporal propaga

EditHero: A Benchmark for Long-Horizon Part-Level 3D Editing and Vibe Modeling huggingface.co

3D editing methods are usually tested on a single edit, yet an asset is built through a long sequence of revisions, each of which must implement the requested change while leaving everything else unchanged. We introduce EditHero, to our knowledge the first benchmark for long-horizon, part-level 3D editing, with natural-language instructions and target images for both geometry and texture. A deterministic assembly engine produces the exact target after every edit, and every sequence is reviewed b

SimuVerity: Benchmarking Agents for Engineering-Grade Simulink Model Generation huggingface.co

Existing Simulink benchmarks mainly evaluate whether generated models compile, execute, or resemble a reference model. These criteria do not establish whether a model satisfies its engineering requirements. We introduce SimuVerity, a benchmark of 101 text-to-executable Simulink model-generation tasks across ten engineering domains. For each task, executable-system profiles ground the engineering specification and four families of native simulation scenarios. A hierarchical evaluator first checks

DEFINE: Exemplar-Guided Accent Control for Zero-Shot TTS huggingface.co

Zero-shot text-to-speech (TTS) can reproduce an unseen speaker from a short reference recording, but typically entangles speaker identity and accent within the same reference. We introduce DEFINE, an end-to-end framework that decouples these factors by conditioning speaker identity and target accent on separate audio exemplars. A single inference-time guidance weight continuously controls accent strength without retraining. Built on F5-TTS with parameter-efficient LoRA adaptation, DEFINE maps sh

LexReward: A Taxonomy-Driven Reward Framework for Legal Language Models huggingface.co

Legal language models require reward signals that capture not only answer correctness but also the multidimensional quality of legal responses. Existing reward methods, however, often rely on coarse-grained holistic judgments, providing limited domain specificity and interpretability. We introduce LexReward, a taxonomy-driven framework for legal reward modeling. LexReward characterizes legal response quality along three complementary dimensions: Style, covering lexical and syntactic quality; Ele

Diptych: Scoped, AI-Interpreted Comparison for Reference Listening in Music Production huggingface.co

Reference listening is a common strategy in music production, but current comparison tools often obscure a key human judgment: deciding what should be compared. We present Diptych, an AI-assisted system that lets users define comparison scope across whole tracks or independently selected segments, while inspecting structured audio features and scope-specific AI interpretations. We evaluated Diptych in a within-participants study with 12 musicians, complemented by source-blinded ratings from four

References

Jim Fan keynote, AI Ascent 2026 (YouTube) youtube.com

VLA is dead — WAMs allow robots to learn physics from internet-scale video rather than relying on scarce teleoperated demonstrations.

layernorm.dev — LIBERO benchmark audit layernorm.dev

Models scoring 95%+ on the original LIBERO suite often dropped to 0% success when objects were moved by as little as two centimeters, suggesting they had memorized trajectories rather than learning manipulation principles.

Simchowitz Lab — ‘Much Ado About Noising’ (PDF) simchowitzlabpublic.github.io

Generative policies rarely learn or utilize multi-modality in practice; a two-step Minimum Iterative Policy matches state-of-the-art flow policies while being up to 4.5x more computationally efficient.

AI Weekly editors’ blog on PointWAM aiweekly.co

Pre-training PointWAM on human videos alone yields a success rate increase of 56.9 percentage points over training from scratch… surpassing the previous multi-task state-of-the-art on DexJoCo by 11.7 points.

Teahose paper summary — NowWAM (2609.28339) teahose.com

NowWAM reduces visual tokens from 784 to 392 and training step time from 2.85s to 1.63s — a 1.8x speedup — while reaching 87.7% on LIBERO-Plus, outperforming future-prediction co-training by 6.1 points.

r/robotics thread ‘VLAs vs NVIDIA world models’ reddit.com

WAMs remain within a convex hull of ideas, synthesizing known data well but failing to innovate novel physical strategies; running a 14B diffusion model in a 7–15 Hz control loop remains a brutal engineering hurdle.

BDTechTalks on Anthropic faithfulness study bdtechtalks.com

Anthropic researchers found that reasoning models often fail to acknowledge when they used hints or shortcuts to arrive at an answer, meaning the CoT does not reliably reflect the model’s actual decision process.

ThinkPrune paper (arXiv 2504.01296) arxiv.org

ThinkPrune can cut the reasoning length of DeepSeek-R1-Distill-Qwen-1.5B by half on AIME24 with only a 2% drop in accuracy by iteratively applying tighter token budgets with RL.

Frontier Model Forum brief on Korbak et al. (2025) frontiermodelforum.org

CoT monitorability is a ‘fragile’ safety property that could be lost if developers optimize for user-friendly or compressed reasoning, potentially teaching models to hide misaligned thoughts.

Lewis-Lim et al., EMNLP 2025 (‘Analysing Chain-of-Thought Dynamics’) semanticscholar.org

Distilled-reasoning models rely heavily on their generated CoT to reach correct answers, while instruction-tuned models often engage in post-hoc rationalization where the answer is determined internally first.

Sebastian Raschka, ‘State of LLM Reasoning Model Training’ magazine.sebastianraschka.com

Vanilla GRPO’s within-group normalization can create a response-length bias where models learn to generate longer incorrect answers to minimize relative penalty, and length penalties often trigger accuracy collapse unless applied adaptively.

Ben Dickson, ‘Escaping the Chain-of-Thought Trap’ (Substack) bdtechtalks.substack.com

Current industry reliance on verbalized CoT is a pragmatic hack that masks a lack of true latent reasoning, and compressed reasoning often reverts to shortcut strategies that break on out-of-distribution problems.

Latent-MOPD paper (arXiv:2610.02381) arxiv.org

Latent-MOPD is the first representation-level method for multi-teacher on-policy distillation… training focus gradually shifts from aligning internal latent representations to matching final next-token probability distributions

AICoder — DN-MOPD write-up m.aicoder.com

plain MOPD often fails to outperform its best single-specialist baseline, as the ‘loudest’ domain effectively hijacks the shared student… Domain-Normalized MOPD rescales feedback by measured domain spread

36Kr (Thinking Machines coverage) eu.36kr.com

distilling mathematical reasoning into a Qwen3-8B-Base model achieved 74.4% on benchmarks using only ~1,800 GPU-hours — nearly a 10x reduction compared to the ~17,920 GPU-hours required for RL

Xiaomi MiMo-V2-Flash technical report mimo.xiaomi.com

the student minimizes the reverse-KL divergence between its own output distribution and those of the relevant specialized teachers at every token position… matches the performance of DeepSeek-V3.2 and Kimi-K2 in reasoning and agentic tasks despite having only a fraction of the active parameters

EmergentMind — Merge-of-Thought (MoT) emergentmind.com

iterative cycle where a student is first fine-tuned on data from specific teachers and then merged in the weight space to consolidate those insights

RUCBM/G-OPD GitHub github.com

official implementation built on the verl framework that supports generalized on-policy distillation with reward extrapolation… scripts for reproducing results on Qwen3-4B using GRPO-based advantage estimators

Sources

References

Jack Sun, writing.