# Supervised Fine-tuning

Planning and control of differentially flat systems rests on a flat output, yet no general procedure writes one down for arbitrary nonlinear dynamics. FlatSeek reverses the reconstruction relation in the definition of flatness: fix an output and its reconstruction maps, close the dynamics differentially, and obtain system–output pairs with known certificates at scale. A Qwen3.5-2B model fine-tuned with LoRA proposes candidates that a symbolic verifier accepts or rejects. FlatSeek solves 74 literature and 171 synthetic systems, against 58 and 127 for the strongest traditional symbolic method.

Planning and control of differentially flat systems rests on a flat output, yet no general procedure writes one down for arbitrary nonlinear dynamics. FlatSeek reverses the reconstruction relation in the definition of flatness: fix an output and its reconstruction maps, close the dynamics differentially, and obtain system–output pairs with known certificates at scale. A Qwen3.5-2B model fine-tuned with LoRA proposes candidates that a symbolic verifier accepts or rejects. FlatSeek solves 74 literature and 171 synthetic systems, against 58 and 127 for the strongest traditional symbolic method.

Preferred Networks has released weights for PLaMo 3 Translate, a translation-specific model, at 0.8B, 2B, 8B and 31B on Hugging Face, along with FP8, W4A16, NVFP4, GGUF and MLX formats for quantized and local inference. The smallest 0.8B model outperforms existing translation-specific models more than 30 times larger on Japanese-English and Japanese-to-multilingual translation, while the 2B model surpasses all existing translation-specific models on English-to-multilingual translation. Training uses two-stage supervised fine-tuning plus DPO, with smaller models distilled from the 31B teacher.

Preferred Networks has released weights for PLaMo 3 Translate, a translation-specific model, at 0.8B, 2B, 8B and 31B on Hugging Face, along with FP8, W4A16, NVFP4, GGUF and MLX formats for quantized and local inference. The smallest 0.8B model outperforms existing translation-specific models more than 30 times larger on Japanese-English and Japanese-to-multilingual translation, while the 2B model surpasses all existing translation-specific models on English-to-multilingual translation. Training uses two-stage supervised fine-tuning plus DPO, with smaller models distilled from the 31B teacher.

AlgoPilot splits language-model problem solving into three steps: decide whether a problem calls for natural-language reasoning, code execution, constraint solving, symbolic planning or mathematical optimization; generate a structured formulation for the chosen paradigm; then let an execution model solve it. With Qwen3-8B as the executor, average accuracy on 13 benchmarks spanning 66 domains rises from 38.4% under direct prompting to 72.9%, 11.7 points above the best large-model prompting baseline GLM-5; with Gemma-4-E4B-it as the executor, average success rises from 39.0% under direct prompting to 60.5%.

AlgoPilot splits language-model problem solving into three steps: decide whether a problem calls for natural-language reasoning, code execution, constraint solving, symbolic planning or mathematical optimization; generate a structured formulation for the chosen paradigm; then let an execution model solve it. With Qwen3-8B as the executor, average accuracy on 13 benchmarks spanning 66 domains rises from 38.4% under direct prompting to 72.9%, 11.7 points above the best large-model prompting baseline GLM-5; with Gemma-4-E4B-it as the executor, average success rises from 39.0% under direct prompting to 60.5%.

On fact-checking claims with randomized poster profiles, the open Tülu-3 post-training recipe makes OLMo-2 32B and Tülu-3 70B judge claims from lower-status posters false more often, an effect under one percentage point but statistically reliable, exceeding training drift and replicating on independent claim sets; Meta's, Alibaba's and Mistral's recipes do not produce it. In OLMo-2 32B the model does not notice the job more but interprets it differently, with a lower-status job pushing the base model toward true and the post-trained model toward false, a flip carried by a single residual-stream direction at supervised fine-tuning. None of seven deployed commercial models doubts lower-status posters more.

On fact-checking claims with randomized poster profiles, the open Tülu-3 post-training recipe makes OLMo-2 32B and Tülu-3 70B judge claims from lower-status posters false more often, an effect under one percentage point but statistically reliable, exceeding training drift and replicating on independent claim sets; Meta's, Alibaba's and Mistral's recipes do not produce it. In OLMo-2 32B the model does not notice the job more but interprets it differently, with a lower-status job pushing the base model toward true and the post-trained model toward false, a flip carried by a single residual-stream direction at supervised fine-tuning. None of seven deployed commercial models doubts lower-status posters more.

Image factuality assessment no longer stops at whether a picture is AI-generated: the EviScene multimodal agent refines its own questions during investigation and reaches 79.05% balanced accuracy and 52.26% evidence sufficiency on 1,527 test images.

Image factuality assessment no longer stops at whether a picture is AI-generated: the EviScene multimodal agent refines its own questions during investigation and reaches 79.05% balanced accuracy and 52.26% evidence sufficiency on 1,527 test images.

Running Hugging Face models on AWS Trainium now rests on a different foundation: TorchNeuron, a native PyTorch backend, exposes Neuron chips as an ordinary device with eager execution, torch.compile and native distributed APIs, replacing the PyTorch XLA-based optimum-neuron. By Hugging Face's comparison, out-of-the-box support among the 100 most-used text-generation architectures rises from 46% with Optimum Neuron v0.4.5 to 89%. The backend enters public beta on October 20, 2026.

Running Hugging Face models on AWS Trainium now rests on a different foundation: TorchNeuron, a native PyTorch backend, exposes Neuron chips as an ordinary device with eager execution, torch.compile and native distributed APIs, replacing the PyTorch XLA-based optimum-neuron. By Hugging Face's comparison, out-of-the-box support among the 100 most-used text-generation architectures rises from 46% with Optimum Neuron v0.4.5 to 89%. The backend enters public beta on October 20, 2026.

Supervised fine-tuning of coding agents no longer has to select only on whether the final patch passes the test suite: ranking trajectories by structure and keeping the top 1,024 raised pass@1 on SWE-Bench Verified from 24.8% for a random subset to 27.6%.

Supervised fine-tuning of coding agents no longer has to select only on whether the final patch passes the test suite: ranking trajectories by structure and keeping the top 1,024 raised pass@1 on SWE-Bench Verified from 24.8% for a random subset to 27.6%.