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今晚，“鸽派加息”？

华尔街见闻美联储今夜加息几成定局，但关键是“加完说什么”。花旗定调此次为“微调”，暗示未来无必然加息，但警告主席沃什若无明确前瞻指引，将引发市场剧烈波动；高盛则直言此次加息缺乏充分经济基础，通胀仅为一次性因素，预计将是一场“无信号加息”。
· 微调蒸馏

“AI安全”的市场意义：推理和后训练算力需求增加20%，拉高行业整体算力成本18%

华尔街见闻AI安全监管正演变为真实可量化的成本冲击，巴克莱最新报告测算，"节奏控制"机制将于2027年为行业算力成本新增逾440亿美元，推动整体成本上升约18%，并在2028年进一步扩大至760亿美元。AI实验室推理毛利率或从80%高位向65%长期中枢收敛，竞争格局同步重塑，GOOGL、META等巨头或借机扩大优势。
· 算力芯片,融资,政策监管,Meta,推理思考,金融,微调蒸馏
AI 资讯
AI 资讯

I trained a 44M parameter quantized LLM from scratch on 45B tokens. It ships in 19.8 MB and runs at ~1,900 tok/s on CPU. [P]

Reddit r/MachineLearning

Three weeks back , i posted SHADOW-250M here. It got 360 upvotes, 293 on r/LocalLLaMA and 94 GitHub stars. Thank you.

That model was 60 MB, ran around 400 tok/s on CPU and could retrieve records from an archive on disk. What it couldn’t do reliably was reason over what it retrieved or compute. So I built a smaller one to experiment with those two problems.

SHADOW-50M is actually 44M parameters, trained from scratch on 45B tokens. 19.8 MB complete model, ~1,900 tok/s on laptop CPU, ~41 MB RAM, ternary {-1,0,+1} weights, 73,880-token vocabulary represented by fixed 512-bit fingerprints instead of a trained embedding, and a 159 KB compiled kernel. It runs completely offline. The same kernel compiled to WebAssembly runs in a browser tab at around 500 tok/s.

This is a proof of concept, not a product.

If SHADOW decides something needs calculation, it writes something like [calc]347*86[eq]. A fixed circuit at the readout takes over and fills in the correct digits in the same token stream. No calculator API, no tool call and nothing pasted back into the prompt. I added circuits for arithmetic, percentages, dates, weekdays, units, counting, sorting, comparisons and a small program machine.

When SHADOW stores a record, it reads it once and writes its attention state to disk at 1 bit, 288 bytes/token. Later it can write [need]condition of Patient P-204, the index finds the record in roughly a microsecond and the stored attention state goes directly back into the model in about 0.03 ms. The text isn’t re-read.

The index is 22 bytes/token and doesn’t use a vector database or embedding model. At 100M tokens, the archive is 28.8 GB plus a 2.2 GB index on disk, while the process uses about 28 MB RAM because everything is memory-mapped and a question only touches the pages it needs.
The index also has a small persistent trail. When retrieved information gets used, that record is reinforced in the index itself. On repeated questions, measured top-1 went from 0.571 to 0.743 without training the model.

I also put it next to Supra-50M-Reasoning, a 51.8M parameter Llama-style bf16 model.
Supra beats SHADOW on every standard benchmark I tested. ARC-Easy: 0.435 vs SHADOW 0.307. PIQA: 0.600 vs 0.570. WikiText-2 perplexity: 165 vs 186.
I’m publishing that because I don’t want to hide where SHADOW is weak.
But those tests aren’t what I trained this model for. Here are some actual greedy outputs, one fresh process per question:
“Tell me a short joke about computers.”
SHADOW: Why did the computer go to the doctor? Because it had a virus.
Supra: Thought for 189 tokens about what computers are and stopped without a joke.
“I have 3 books and I bought 5 more. How many books do I have now?”
SHADOW: You have 8 books.
Supra: Books often come from collections of short stories, poems, or other forms of literature.
“My bill is 240 dollars. What is 15 percent of that?”
SHADOW: 36 dollars.
Supra: 15% ($240) is $250. It’s $150 because 20% of it is $200.
“What date is 45 days after December 20, 2026?”
SHADOW: February 3, 2027.
Supra: The German government introduced a new date called March 22nd that precede February 25th in 1900.
Eight records stored, one of them “Patient P-204 condition: asthma”.
“What is the condition of Patient P-204?”
SHADOW: From disk: The condition of Patient P-204 is asthma.
Supra, with the records in its prompt: The patient experiences asthma when they get too much sleep.
“What is the condition of Patient Z-999?” No such record exists.
SHADOW: There is no record of Patient Z-999.
Supra: The condition of an allergic reaction occurs when the immune system mistakenly attacks specific cells on the body.
All 11 side-by-side questions, scripts and traces are in the repo.
On size, Supra is 103.6 MB in bf16 and 56.2 MB at 8-bit. Below that it starts losing quality. Int4 takes its perplexity from 165 to 193 and ternary breaks it. Its vocabulary is 32,000 tokens. SHADOW carries 73,880 tokens in a 4.7 MB frozen table.

Something unexpected happened with that frozen table too.
The first SHADOW-50M release was missing about 8,600 English word pieces. Lowercase “fitzgerald”, for example, could reach the model as “fitz”. I tried fixing it through fine-tuning, but every run that learned the missing words broke something else.
So I added the 8,600 missing rows directly to the frozen fingerprint table. No training. Same weights.
All 34/34 previously published answers stayed unchanged, while the model could now read many of the new pieces. The table scores 0.594 Spearman against human word-similarity ratings versus -0.057 for random codes.
A trained embedding can’t simply accept thousands of new rows without training. A frozen table can.

I also built four harnesses that put this tiny model next to larger models.
My favourite is video memory. Gemma 3 4B watches a ten-minute film once, one frame every two seconds, and writes 298 little descriptions such as “Moment M-0039 scene: A chubby white rabbit reaches for a purple butterfly.”
Then Gemma leaves.
SHADOW keeps those 298 moments as memory on disk. Afterwards I can ask the 20 MB model what happened at a particular moment, by number, by time or by what appeared in it. It answers from memory with the record quoted, without the film and without Gemma. It scored 55-56/60 across those query types on a laptop in about 42 MB RAM.
The other three experiments: an inventory of 1,600 records got 159/160, with all 20 questions about items never stored correctly returning “no record”; SHADOW as a draft model for Qwen3-32B took llama.cpp generation from 19.7 to 28.5 tok/s while Qwen still chose every final token; and an MCP memory server let Qwen3-14B store facts mid-chat and later retrieve 5/5 with the original records quoted.

There are plenty of shortcomings. General knowledge is thin. Creative writing isn’t good. Seven-digit operands sometimes get copied incorrectly. A large archive can occasionally pull an unrelated record into a question carrying a number. They’re documented in the repo next to the successful results.

And one thing happened after my last post that I really didn’t expect.
Someone called engram-forge sent a pull request to SHADOW-250M containing a CUDA engine, a quantization tutorial, and then a talking Peppa Pig plush toy with SHADOW inside it.
Microphone, small speaker, ~$35 board. You talk to the toy, it listens, SHADOW generates the answer locally and the toy talks back. No cloud, no account, no internet.
I haven’t merged the ~11,000 lines yet because I can’t properly verify that much CUDA myself. When the demo is finished I’ll keep it under engram-forge’s name.
I never imagined one of these models living inside a stuffed toy on someone’s shelf .Thanks

I’m not saying a 20 MB model beats normal LLMs. It doesn’t. I’m trying to find out how much useful behaviour can fit into a tiny local model when computation and persistent memory are treated differently.
Everything is MIT licensed. The master weights and fine-tuning/export kit are public. Next I’m releasing the training code, dataset, frozen table and a proper write-up of how I built it, including the costs, failed experiments and mistakes.

Code:
https://github.com/QLNI/SHADOW-50M-Instruct
Weights:
https://huggingface.co/QLNI/shadow-50m-instruct
Run it in your browser:
https://qlni.github.io/SHADOW-50M-Instruct/web

submitted by &#32
2026-09-15 12:59:37 · 大模型,算力芯片,开源,Google,Meta,NVIDIA,阿里巴巴,Agent智能体,推理思考,搜索RAG,Transformer,扩散模型,微调蒸馏,模型评测,向量数据库,提示工程,招聘HR,榜单评测,开发者生态
AI 资讯

Learning to solve hard problems in RL for LLMs by never giving up

Hacker NewsComments
· 大模型,AI应用,开源,OpenAI,阿里巴巴,DeepSeek,Agent智能体,推理思考,搜索RAG,办公效率,强化学习,微调蒸馏,模型评测,提示工程,招聘HR,榜单评测,论文
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Information-Theoretic Bounds for Sparse Covariance Estimation in the Vertical-Split Distributed Model

arXiv stat.MLarXiv:2606.07124v2 Announce Type: replace-cross Abstract: We study the minimax estimation error for distributed covariance matrix estimation in the vertical-split (feature-split) setting, where two agents each observe different coordinates of~$m$ i.i.d.\ sub-Gaussian samples and communicate a limited number of bits to a central server. While \cite{rahmani2025fundamental} established nearly tight bounds for dense (unstructured) cross-covariance matrices, we investigate whether imposing elementwise $s$-sparsity on the cross-covariance $C_{21}$ can reduce the required communication and sample complexity. In contrast to the horizontal-split setting, where \cite{braverman2016communication} showed that sparsity does \emph{not} reduce communication cost for mean estimation, we prove that sparsity \emph{does} help for cross-covariance estimation in the vertical split. Specifically, for sufficiently large $d_1d_2/s'$ and $0<\varepsilon<\sigma^2\sqrt{s'}/32$, any scheme achieving expected Frobenius distortion at most $\varepsilon$ must satisfy $B_k = \Omega(\sigma^4 d_k\, s' \log(d_1 d_2/s')/\varepsilon^2)$ and $m = \Omega(\sigma^4\, s' \log(d_1 d_2/s')/\varepsilon^2)$ for cross-covariance estimation, where $s' = s \wedge d_{\min}$. For the $1$-sparse case, our achievable scheme reduces the $d_1d_2$ factor in the dense communication rate to $\log(d_1d_2)$, up to polylogarithmic factors, for the cross-covariance communication component in the matching regime. Our lower bounds are established via Fano's method with an explicit sparse packing using a Varshamov--Gilbert-type argument for signed partial permutation matrices combined with the Conditional Strong Data Processing Inequality of \cite{rahmani2025fundamental}. We show that the communication lower bound is tight up to polylogarithmic factors under the conditions of Remark~\ref{rem:achievmatch}, using an achievable scheme based on covering-net quantization and entry-wise hard thresholding.
2026-09-16 04:00:00 · 大模型,AI应用,Agent智能体,扩散模型,微调蒸馏,招聘HR,论文
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Shuttling Compiler for Trapped-Ion Quantum Computers Based on Fine-Tuned Large Language Models

arXiv cs.LGarXiv:2512.18021v4 Announce Type: replace-cross Abstract: In trapped-ion quantum computers, qubits must be shuttled between segments to interact. The routing logic that schedules these movements is written by hand for every new trap architecture. We present shuttling compilers based on five large language models (LLMs). Each LLM is fine-tuned on shuttling schedules produced by hand-coded heuristics for linear and branched one-dimensional trap architectures. We investigate how the shuttling operation counts of their schedules compare with those of the heuristics and how far they generalize to unseen architectures. For circuits of up to 16 qubits, the fine-tuned LLMs generate valid schedules on both training architectures, more often the fewer qubits a circuit has. In 12% of the compilations yielding a schedule, the best of ten runs needs up to 21% fewer operations than the heuristic baselines, after a rule-based post-processing step. A single run of one fine-tuned LLM produces a valid shuttling schedule for a previously unseen four-way branched architecture. This is preliminary evidence of cross-architecture generalization. On two other unseen architectures no LLM produces a valid schedule. Thus, LLM-learned shuttling compilation is feasible, and we show how far it currently reaches.
2026-09-16 04:00:00 · 大模型,微调蒸馏,论文
AI 资讯

CBW: Towards Dataset Ownership Verification for Speaker Verification via Clustering-based Backdoor Watermarking

arXiv cs.LGarXiv:2503.05794v4 Announce Type: replace-cross Abstract: Speaker verification models are trained on large-scale public datasets whose licenses usually prohibit unauthorized commercial use, yet such infringement is difficult to detect or deter. Dataset ownership verification (DOV) is the mainstream countermeasure: it can watermark a dataset with backdoor attacks so that models trained on it exhibit owner-specified behaviors. However, existing DOV methods presuppose a closed label space fixed at watermarking time, whereas in open-set speaker verification the identities that a deployed model accepts are enrolled by third parties after release and are never observed by the dataset owner. We show that straightforward adaptations fail in two characteristic modes, and accordingly distill three requirements for an effective watermark, namely identity agnosticism, coverage, and fidelity, together with an intrinsic tension between the latter two. Our clustering-based backdoor watermark (CBW) resolves this tension by partitioning training speakers into clusters by feature similarity and implanting a distinct trigger for each cluster, so that each trigger covers one region of the speaker embedding space while the trigger set is designed to jointly cover it. We further develop paired hypothesis tests for ownership verification under both the similarity-available and the decision-only black-box settings at the 1-to-1 and 1-to-$N$ enrollment scales, and theoretically characterize when the audit succeeds, including an exact small-sample certificate and the effect of the enrollment size. Extensive experiments on benchmark datasets and representative models verify the effectiveness of our CBW, its resistance to watermark-removal attacks, and its transferability across model structures. Code is at https://github.com/Radiant0726/CBW/tree/master.
2026-09-16 04:00:00 · 大模型,AI应用,开源,搜索RAG,扩散模型,强化学习,微调蒸馏,模型评测,向量数据库,招聘HR,论文
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PRISM: Parallel Residual Iterative Sequence Model

arXiv cs.LGarXiv:2602.10796v4 Announce Type: replace Abstract: Generative sequence modeling faces a fundamental tension between the expressivity of Transformers and the efficiency of linear sequence models. Existing efficient architectures are theoretically bounded by shallow, single-step linear updates, while powerful iterative methods like Test-Time Training (TTT) break hardware parallelism due to two dimensions of serial dependency: token-level state reliance and step-level iteration loops. We propose PRISM (Parallel Residual Iterative Sequence Model) to resolve this tension. PRISM explicitly approximates the expressive gate-residual-direction iteration pattern of TTT in a parallelizable form. We employ a Write-Forget Decoupling strategy that isolates non-linearity within the injection operator. To bypass the serial dependency of explicit solvers, PRISM utilizes a two-stage proxy architecture: a short-convolution anchors the initial residual using local history energy, while a learned predictor estimates the refinement updates directly from the input. This design distills structural patterns associated with iterative correction into a parallelizable feedforward operator. Theoretically, we prove that this formulation achieves Rank-$L$ accumulation, structurally expanding the update scheme beyond the single-step Rank-$1$ bottleneck. Empirically, it achieves comparable performance to explicit optimization methods while achieving \textbf{174x higher throughput}. Codes are available in https://github.com/gpr-prism/prism/.
2026-09-16 04:00:00 · 开源,Transformer,微调蒸馏,端侧AI,招聘HR,网络安全,论文
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Beyond Measurement Metrics: A Human-Centered Framework for Semantic Validation of Network Traffic Classification

arXiv cs.LGarXiv:2609.17014v1 Announce Type: cross Abstract: Machine learning (ML) has become the dominant approach for network traffic classification, achieving very high predictive performance. However, a model is only valuable if it learns semantically meaningful and trustworthy patterns rather than exploiting spurious correlations. Conventional evaluation practices predominantly assess predictive performance. Consequently, whether the model relies on semantically meaningful patterns remains unknown. To address these challenges, we adapt the knowledge generation framework for network traffic classification. The adapted framework combines data, ML models, explainability, visualization, and expert reasoning to support the iterative exploration, verification, and refinement of model behavior and data preprocessing. The framework is grounded in findings from the literature, benchmark dataset analyses, practical experience with XAI-based traffic classification, and expert feedback, providing practical guidance for semantic model validation. By complementing predictive performance with semantic validation and human expertise, the proposed framework supports the development of network traffic classification models that are not only accurate but also robust and trustworthy.
2026-09-16 04:00:00 · xAI,推理思考,强化学习,微调蒸馏,模型评测,论文
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Causal Discovery via Transformed Low-Rank Quantile Surfaces

arXiv cs.LGarXiv:2609.16931v1 Announce Type: cross Abstract: We propose Low-Rank Quantile Surfaces (LRQS), a bivariate causal model in which, in the causal direction, an unknown monotone transformation of the conditional quantile surface admits a low-rank functional decomposition. LRQS subsumes location-scale noise models and post-nonlinear heteroscedastic noise models, while allowing multiple quantile bases to represent changes beyond location-scale effects. We prove generic identifiability of LRQS: the transformed quantile surface is low rank in the causal direction, whereas reverse representability under the corresponding constraints occurs only for exceptional, fine-tuned cause marginals. We provide a simple-yet-powerful causal score using a nonparametric fitting procedure that alternates between rank-constrained approximation of discretized quantile surfaces and isotonic estimation of the unknown monotone transformation. Experiments on synthetic mechanisms with higher-rank distributional shape variation and strong nonlinear distortions, together with standard bivariate benchmarks, show that LRQS is especially effective when conditional distributional shape or observation distortion goes beyond existing location-scale assumptions.
2026-09-16 04:00:00 · 扩散模型,微调蒸馏,模型评测,论文
AI 资讯

Towards Surrogate Based Dequantization of Quantum Reinforcement Learning

arXiv cs.LGarXiv:2609.16266v1 Announce Type: cross Abstract: In recent years, the utility of parameterized quantum circuits as function approximators has been widely studied. In the context of reinforcement learning, this approach has led to variational quantum algorithms such as quantum Q-learning. While these methods show promising empirical results, and can provide provable advantages for artificial problems, it remains unclear whether they can provide a provable quantum advantage over classical approaches for problems of practical relevance. A natural way to investigate this question is through the lens of dequantization: The construction of efficient classical algorithms capable of matching the performance of quantum variational methods. Building on recent kernel-based dequantization results for supervised learning, we take steps towards extending this surrogate-based dequantization program to reinforcement learning. Specifically, we study the simplified setting of reinforcement learning with a uniform generative model in which uniformly random state-action samples are available, which models the regime of sampling from a large experience replay buffer after sufficient exploration. Within this setting, we provide finite sample guarantees for classical kernelized Fitted Q-Iteration, with classical kernels designed to match the inductive bias of particular parameterized quantum circuits. Using these results, we then provide a set of sufficient conditions, on the data-encoding strategy of a parameterized quantum circuit, the corresponding classical kernel, and the problem structure, under which kernelized Fitted Q-Iteration provides a meaningful dequantization of quantum Q-learning, in this simplified setting. Apart from providing rigorous dequantization guarantees when these conditions are met, these results also motivate the use of kernelized fitted Q-iteration as a dequantization heuristic when these sufficient conditions cannot be verified.
2026-09-16 04:00:00 · 扩散模型,强化学习,微调蒸馏,招聘HR,论文
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Compute-Optimal Pretrain--Fine-tune in Ridge Gradient Descent

arXiv cs.LGarXiv:2609.16262v1 Announce Type: cross Abstract: Pretraining followed by fine-tuning introduces a compute-allocation problem: under a fixed training budget, compute spent improving the upstream objective reduces the compute available for downstream adaptation. Despite its practical importance, this trade-off is not yet well understood theoretically, even in simple models. In this paper, we cast this allocation as a compute-split problem under a two-stage pretrain--fine-tune procedure with fixed total optimisation budget, using regularised least squares trained by gradient descent as a tractable setting. We characterise the optimal split under data-dependent evaluation geometries induced by the fine-tuning problem. Our results show that the allocation depends on how pretraining directions affect fine-tuning predictions and how fine-tuning shifts are seen through downstream data geometry. In particular, the relevant quantities are determined by prediction-relevant spectral components of the pretraining and fine-tuning empirical covariances. Technically, the analysis relies on a basis-invariant, eigenspace-level spectral decomposition, together with perturbative control of the non-commuting pretraining and fine-tuning dynamics.
2026-09-16 04:00:00 · 微调蒸馏,预训练,招聘HR,论文
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Is INT8 Portable? A Cross-Platform Measurement Study of Quantized Inference on Embedded and Automotive Accelerators

arXiv cs.LGarXiv:2609.16085v1 Announce Type: cross Abstract: Eight-bit integer (INT8) post-training quantization is the default recipe for edge deployment, under a widely held assumption: INT8 makes inference faster at a small, predictable accuracy cost, and a model quantized once can be carried to any target. We test that assumption with a controlled measurement study across seven hardware classes -- ARM and x86 CPUs, a discrete GPU, an NVIDIA Jetson AGX Orin iGPU and its NVDLA cores, and two vendor NPUs (Qualcomm Hexagon HTP, DEEPX DX-M1) -- holding the ONNX artifact and the quantization scales fixed so the integer kernel or ISA is the only free variable. Portability fails on three axes. (1) The sign of the INT8 speedup is set by the CPU's dot-product ISA (ARM dotprod/SDOT, x86 VNNI): cores that have it speed up by up to 2.1x, cores that lack it slow down by 1.7x, for the identical model and runtime. (2) INT8 outputs are not portable, and the rule is an invariance rather than a gradient: FP32 predictions are bit-identical for every pair (1000/1000), while INT8 predictions agree 1000/1000 exactly when two targets share an integer kernel and 958-965/1000 whenever they do not -- independent of whether the boundary is CPU<->CPU or CPU<->accelerator, and invisible to top-1 accuracy, which is preserved. (3) Vendor NPUs own quantization: a bring-your-own QDQ graph fails silently on one NPU (external scales ignored, accuracy 0.75 -> 0.005 while it compiles, profiles and runs without error) and loudly on the other (the compiler refuses the graph), so only the vendor's native path yields a correct engine. We further show that edge-NPU latency regimes are set by output/device-to-host transfer size rather than compute, and locate the transition with a fixed-compute sweep. We release the scripts and 32 reports. "Quantize once, deploy anywhere" is unsafe for embedded and automotive deployment, where per-input determinism and redundancy matter.
2026-09-16 04:00:00 · 算力芯片,Google,NVIDIA,微调蒸馏,端侧AI,招聘HR,榜单评测,论文
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Molecular representation shapes the balance between target fidelity and exploration in flow based polymer generation

arXiv cs.LGarXiv:2609.16028v1 Announce Type: cross Abstract: Designing polymers with targeted properties requires navigating vast chemical spaces from limited labeled data. Here we introduce PolyLatentFlow, a framework based on continuous-time flow matching in latent space for unconditional and conditional polymer generation, together with LlamaUni, a multimodal representation combining polymer sequence and 3D structural information. In unconditional generation, PolyLatentFlow with LlamaUni produced the largest yield of valid candidates novel relative to PolyInfo among the evaluated unconditional generators while maintaining high diversity. For $T_g$ conditioning, generated property distributions shifted systematically across a 200 {\deg}C target range. In multi-property tasks, molecular representations showed similar surrogate target fidelity but differed markedly in validity, training-set replay, and structural proximity to labeled polymers. PolyLatentFlow with LlamaUni consistently combined high validity with low replay and achieved the largest per-attempt yield of nonreplayed target hits for CO$_2$/N$_2$ conditioning. These results demonstrate latent space flow matching for polymer inverse design and identify molecular representation as a key determinant of target control and exploration beyond labeled chemistry.
2026-09-16 04:00:00 · 大模型,Meta,多模态,扩散模型,微调蒸馏,论文
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Coupled Calibration and Learning: Mitigating Teacher Bias in LLM Distillation without Target-Domain Reward Feedback

arXiv cs.LGarXiv:2609.17474v1 Announce Type: new Abstract: Large language model (LLM) distillation aims to transfer the capabilities of a powerful teacher to a smaller student. Direct imitation, however, can also transfer the teacher's systematic bias and errors. This challenge is particularly pronounced under covariate shift, when the teacher's reliability on target questions is uncertain and target-domain reward feedback is unavailable. We propose Coupled Calibration and Learning (CCL), an LLM distillation algorithm that couples teacher calibration with student updates through token-level branching, using reward feedback only on source questions. Each iteration calibrates the teacher using source feedback and then uses the calibrated teacher to train the student on target questions. The updated student, in turn, informs subsequent calibration. In an autoregressive policy framework, we prove that the output student's expected average Kullback-Leibler divergence to the oracle student converges to zero at a polynomial rate in the number of iterations. The oracle maximizes the true reference-regularized target reward within the student class, which need not represent the unrestricted optimal policy. Our analysis quantifies the progress of projected student gradient updates while controlling the error in teacher calibration. We further establish a separation from regularized direct matching: its error relative to the oracle student can remain bounded away from zero even when the teacher achieves higher regularized target reward than every student policy. These results demonstrate that LLM distillation can overcome persistent teacher bias and recover the optimal student through coupled calibration and learning, without target-domain reward feedback.
2026-09-16 04:00:00 · 大模型,算力芯片,AI应用,Google,搜索RAG,微调蒸馏,招聘HR,论文
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Personalized Federated Learning through Global Knowledge Distillation and Local Head Adaptation

arXiv cs.LGarXiv:2609.17284v1 Announce Type: new Abstract: Statistical heterogeneity limits federated learning when a single global classifier cannot represent client-specific label distributions. In this work, we propose Personalized Federated Knowledge Distillation with Head Adaptation (pFedKDH), which aggregates only the shared backbone, keeps persistent client-specific heads, and uses a recalibrated global head as a teacher during local training. Across MNIST, Fashion-MNIST, CIFAR10, and CIFAR100 under class-wise Dirichlet partitions, pFedKDH obtains the best accuracy in most settings, with accuracy gaps up to 37.67\% over the weakest baseline and consistently low standard deviation across repetitions. Component-wise diagnostics and convergence results support the role of persistent heads and distillation-guided local optimization under label-skewed data.
2026-09-16 04:00:00 · 强化学习,微调蒸馏,联邦学习,招聘HR,论文
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Beyond Token-Local Imitation: Reward-Compatible Temporal Credit Assignment for On-Policy Distillation

arXiv cs.LGarXiv:2609.16937v1 Announce Type: new Abstract: On-policy distillation (OPD) has emerged as an effective approach for large language model post-training, yet existing objectives face a trade-off between objective fidelity and optimization stability. Token-level OPD provides stable but local supervision, whereas sequence-level OPD captures future credit at the cost of horizon-dependent variance. We establish a unified temporal-credit view of these formulations, showing that practical token-level OPD can be interpreted as a temporal approximation to the sequence-level reverse-KL gradient. Building on this connection, we propose $\gamma$OPD, which uses discounted temporal credit assignment to balance long-horizon supervision and optimization stability, while admitting a horizon-independent variance bound. We further develop a reward-compatible bounded mixing (RBM) mechanism for $\gamma\mathrm{OPD}$ that balances verifiable outcome feedback with the discounted OPD advantage to move beyond purely teacher-dependent optimization. Experiments on mathematical and code reasoning demonstrate consistent improvements over existing OPD methods across vanilla, size-mismatched, and multi-teacher distillation settings.
2026-09-16 04:00:00 · 推理思考,扩散模型,微调蒸馏,招聘HR,论文
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