Note obs. Diffusion LMs often settle on the right answer early, then waste compute refining after their beliefs have effectively converged. innov. A training-free decoding rule that watches a simple confidence-gap signal to stop iterating and commit, cutting steps by up to ~3.4× while preserving accuracy (near-full correctness at half steps on GSM8K/MMLU).

Note obs. Token-level masked diffusion LMs can't use KV caching and must learn combinatorial token dependencies, raising compute and hurting coherence vs AR decoding. innov. Slot-level plan-and-infill: diffusion picks weakly dependent fixed-length slots; an AR infiller decodes selected slots in parallel under a unified causal/KV-cache setup, reporting >18× speedup vs prior MDMs and ~2.33× vs strong ARMs while narrowing the accuracy gap.

Note obs. Standard RLHF rewards treat multi-constraint instructions as flat; partial failures are not propagated, so models learn weak adherence to ordered and conditional requirements. innov. Build LsrInstruct with explicit parallel/sequential/conditional constraint structures, and a structure-aware reward (avg aggregation, failure-penalty propagation, branch selection) that improves in- and out-of-domain instruction following and general reasoning.

Note obs. Common "sequence distillation" (SFT on teacher outputs) treats distillation as filtered imitation, underrepresenting the teacher's sequence distribution, mismatching it to student capacity, and adding exposure bias from teacher forcing vs AR inference. innov. Distribution-aligned sequence distillation with explicit teacher-student interaction; reports DASD-4B-Thinking as SOTA at its scale using ~448K samples, with model and dataset released.

Note obs. Static tool libraries are intrinsically incomplete in scientific domains, so long-tail tasks fail even when reasoning is competent. innov. A test-time loop that synthesizes, verifies, and iteratively evolves executable tools, evaluated on a benchmark that couples tasks with evolved tools.

Note obs. Personalization can entangle user-history signals with factual representations, causing answers to track prior context over objective truth. innov. An inference-time steering method that preserves personalization while suppressing factual distortion, plus a benchmark for joint factual and personalized QA.

Note obs. Discrete CoT increases length and bandwidth, but uncertainty is often localized to a small set of plausible next steps. innov. Sample K candidate tokens per step and merge their embeddings into one continuous multiplex token, enabling branch-and-merge reasoning optimized with on-policy RL.

Note obs. Code diffusion models often lag AR under matched budgets, in part due to unstable training and inefficient knowledge uptake. innov. Reuse an AR code pipeline but add block-diffusion continual pretraining with tailored warmup and noise scheduling to improve code benchmark performance.

Note obs. After reasoning distillation, it is unclear which parts of a student's reasoning are inherited from the teacher versus reverting to the base student. innov. A provenance-tracing analysis that compares teacher/base/distilled probabilities at the sentence/action level and uses the signal to guide principled data selection.

Note obs. Raw-text pretraining does not explicitly optimize linguistic competence, so grammatical generalizations emerge indirectly and slowly. innov. Transform text into structured language-learning tasks and mix them into pretraining to accelerate linguistic competence acquisition without specializing away from general tasks.

Note obs. External judges and text-based self-critique add compute and can correlate weakly with true correctness. innov. A small auxiliary predictor that reads hidden-state and attention traces from a frozen model to estimate correctness during generation with negligible overhead.

Note obs. Next-token-only training underutilizes supervision in the near future context and limits sample efficiency. innov. Multiple future-token prediction heads on a shared trunk, improving capability and enabling multi-token generation speedups at inference.

Note obs. Interpretability evidence suggests reasoning computation concentrates in a subset of layers, but standard decoding traverses the full stack once. innov. Train the model to iterate a selected mid-layer block as a recursive latent "thinking" loop, then decode back to tokens for test-time reasoning scaling.

Note obs. Bidirectional attention in diffusion LMs prevents standard KV caching, making inference dominated by repeated recomputation across denoising steps. innov. A block-wise approximate KV cache reuses activations across steps, plus a confidence-aware parallel decoding fix for dependency disruption; reports large end-to-end speedups on diffusion LM baselines under long-generation settings.

Note obs. Token-level CoT scales compute by emitting more text, which costs latency and can be bottlenecked by context length. innov. A recurrent-depth LM unrolls a shared block to arbitrary depth at test time, scaling latent compute without extra tokens; a 3.5B prototype shows reasoning gains up to a compute load comparable to ~50B params.

Note obs. Standard LLMs mostly learn to "think" after pretraining (prompting/CoT), so pretraining compute is underused for learned multi-step manipulation. innov. Looped Language Models add iterative latent computation plus an entropy-regularized depth allocator during pretraining; the Ouro 1.4B/2.6B models report performance comparable to up to ~12B baselines across many evals.

Note obs. One forward pass per token forces the model to commit early, while deeper deliberation typically requires emitting more tokens. innov. "Pondering" repeats forward passes within a token step by feeding back a probability-weighted embedding mixture; learned self-supervised during pretraining, and reports that pondering-enhanced small models can match much larger vanilla counterparts.

Note obs. Discrete CoT explores reasoning paths one token at a time, which limits branching and often inflates token budget. innov. Generate "soft" concept tokens as embedding mixtures (continuous-space reasoning) without extra training; reports pass@1 gains (up to ~2.48) while reducing token usage (up to ~22.4%) on math/coding benchmarks.

Note obs. Hard-token CoT constrains intermediate reasoning to discrete vocabulary and can be inefficient or brittle. innov. A lightweight assistant proposes soft-thought tokens that are projected into the base LLM’s representation space, improving reasoning with parameter-efficient tuning while leaving the base model unchanged.

Note obs. Continuous latent thoughts can be high-information, but a fixed latent trace limits exploration compared to multi-sample discrete CoT. innov. Perturb latent thoughts using specialized initial tokens and train for diversity (contrastive) so test-time scaling explores multiple soft-thought paths; reports stronger gains than SoftCoT baselines and compatibility with self-consistency.

Note obs. Diffusion LMs can parallelize but often lag in likelihood modeling and can be constrained to fixed-length generation setups. innov. Block diffusion models define an AR distribution over blocks and run diffusion within each block, enabling arbitrary-length generation plus KV caching and parallel sampling; reports strong diffusion-model language-modeling results.

Note obs. Diffusion decoding recomputes QKV for all tokens at each step, even though many token representations change slowly over denoising. innov. Delayed/conditioned cache reuse (dKV-Cache) with two variants: near-lossless decoding-oriented caching and a more aggressive greedy mode; reports ~2–10x inference speedups across general, math, and code benchmarks.

Note obs. Even with block diffusion, recomputing KV everywhere remains redundant because many tokens/layers exhibit limited change across steps. innov. A training-free dual adaptive caching framework selectively updates KV states for a subset of tokens per step and reuses the rest, aiming to preserve quality while substantially reducing redundant compute in diffusion decoding.

Note obs. KV states drift unevenly across layers and steps, yet diffusion decoders typically refresh everything uniformly. innov. Elastic-Cache uses an attention-aware drift test plus a depth-aware refresh schedule to recompute only where needed; reports substantial speedups on math/code tasks (including very large gains on long sequences) with negligible quality loss.

Note obs. Discrete diffusion for text can require hundreds to thousands of model evaluations, so parallelism does not automatically translate into low latency. innov. Few-Step Discrete Flow-Matching trains step-budget consistency so small step counts approximate long trajectories; reports that ~8 steps can match a 1024-step baseline perplexity for long generation, enabling very large sampling speedups.

Note obs. Fixed confidence-threshold heuristics for parallel decoding are input-agnostic and miss optimal speed/quality trade-offs. innov. Train a lightweight filter to predict whether a token is already final, enabling adaptive unmasking plus end-of-text prediction; reports up to ~22.6x speedup without performance drop (and higher with KV-cache) on the LLaDA benchmark.

Note obs. Prior parallel decoding prioritizes high-confidence tokens, but those tokens carry little information, slowing progress per round. innov. A bits-to-rounds principle links required rounds to total information and per-round budget, motivating Explore-Then-Exploit decoding that targets uncertain tokens to trigger cascades; empirically reduces rounds without quality loss.

Note obs. Many diffusion decoding strategies are reactive and do not use bidirectional context to enforce a global generation trajectory. innov. Plan-Verify-Fill builds a hierarchical skeleton via high-leverage anchors and uses quantitative verification to stop deliberation when marginal value drops; reports up to ~65% fewer function evaluations on LLaDA/Dream instruct models.

Note obs. Decoder-only LMs re-run early/middle layers for every next token even when those layers have already produced a representation sufficient to support short-range continuation. innov. Direct Multi-Token Decoding reuses late layers to emit multiple tokens per cycle, reducing full forward passes; a fine-tuned Qwen3-4B variant reports up to ~2x speedup with only minor performance loss.

Note obs. Self-evolving reasoning systems are often bottlenecked by needing large, curated task/label sets to bootstrap training signals. innov. A data-free/self-evolving setup that generates and learns from its own experiences to grow reasoning behavior; positioned as reducing dependence on curated tasks and enabling iterative capability growth without human-labeled corpora.

Note obs. Synthetic verifiable data pipelines often fail because verification artifacts are weak/trivial, so they do not reliably separate strong from weak solutions. innov. An evolutionary, task-agnostic framework that jointly synthesizes problems, candidate solutions, and executably-checkable verifiers, iteratively improving strategies via consistency-based evaluation; reports gains under verifiable-RL and distillation setups.

Note obs. Large instruction corpora can still be shallow (low complexity) or narrow (poor rare-domain coverage), so scaling sample count alone plateaus. innov. A framework combining hierarchical labeling, informative seed selection, evolutionary synthesis, and deficiency diagnosis to iteratively expand coverage and depth; produces a ~1.5M-instruction set and reports improved instruction-following on multiple models.

Note obs. When prompts contain many explicit requirements (often >10), instruction-following breaks down because evaluation and training data under-represent that regime. innov. Synthesize constraint-heavy examples from real prompts and add automatic verification (rule-based for quantitative constraints plus qualitative validation) to build RECAST-30K; fine-tuning yields improved complex instruction-following and supports reward construction.

Note obs. Scalar/pairwise reward models compress preferences too aggressively and miss multi-dimensional criteria that humans use in evaluation. innov. A large (prompt, rubric) collection plus contrastive rubric generation and noise filtering to train rubric-conditioned reward models; reports a ~6.8% gain over size-matched reward baselines with transfer to downstream policy performance.