Finisky Garden

A user asks, “Can I drink seawater after boiling it?” A document about the evaporation of salt water may help answer the question: water evaporates when heated, while salt remains. The document says nothing directly about drinking seawater. Recognizing its relevance requires considering whether boiling removes salt.

Reasoning-intensive retrieval (RIR) concerns retrieval tasks where assessing relevance requires inference. The difficulty is that a system needs some understanding of the knowledge required to solve a problem before it has found the useful documents. In a pipeline with a fixed set of candidates, evidence missed during retrieval remains unavailable to the answer generator, however much reasoning it does afterward.

Hard Negatives, Hard Lessons (Findings of EMNLP 2025) prunes the BGE retrieval training collection from 15 datasets to 7, reducing it from approximately 1.6 million training pairs to 680,000. Despite this reduction by a factor of about 2.35, E5-base gains approximately 1 point in average nDCG@10 across 14 BEIR datasets.

The remaining data still contains labeling errors. Some passages answer the query but are treated as hard negatives during training. The paper proposes RLHN (ReLabeling Hard Negatives), which uses two LLM stages to identify these passages and relabel them as positives. Compared with training on the pruned data with its original labels, relabeling improves E5-base and Qwen2.5-7B by approximately 0.7 and 1.4 points on BEIR, respectively.

RegMix solved pretraining mixture optimization with 512 tiny proxy models plus LightGBM regression, but its assumption is static: fit once, get one globally optimal mixture. When the underlying data pool shifts (domains added or removed, distribution drift), the entire regression needs to be rerun. At the SFT stage this problem is worse, because SFT data pools and domain definitions change frequently.

CausalMix ( Data Mixture as Causal Inference for Language Model Training , 2026/07) recasts mixture optimization as a causal inference problem. The difference: RegMix learns a global mapping $T \to Y$ (mixture to performance), while CausalMix learns a conditional causal effect $\theta_0(X)$ (the marginal return of each domain under the current data state $X$). By orthogonalizing confounding effects of the data state via Double Machine Learning, the causal model does not need new proxy experiments when the data pool changes.

After fitting the causal model on 512 Qwen2.5-0.5B runs over tulu-3-sft-mixture, extrapolating to 800K samples for a 7B model, CausalMix-S reaches AvgDev 62.28 (RegMix 60.14, DMO 60.35) and CausalMix-A reaches AvgUns 49.09 (RegMix 48.12, DMO 48.98). On transfer to a completely different Qwen3-4B + AM-Thinking long-CoT dataset, CausalMix averages 66.66, versus RegMix 61.40 and DMO 63.47.

Multi-task preference alignment (helpfulness, harmlessness, coding, etc.) faces a practical engineering problem: how to choose the data mixture. Uniform mixing lets large datasets dominate the gradient, while equal task weighting may waste capacity on easy tasks. The common practice of running extensive ablation studies or manually tuning ratios is expensive and often suboptimal.

AutoMixAlign (AutoMixAlign: Adaptive Data Mixing for Multi-Task Preference Optimization in LLMs, ACL 2025) takes a different approach: first train a specialist model for each task as a reference baseline, then dynamically adjust task weights or sampling probabilities during generalist training via minimax optimization, prioritizing tasks where the generalist-specialist loss gap is largest. On Zephyr-7B, multi-task average performance improves by up to 9.42% while avoiding the “one task degrades” failure mode common in standard DPO mixed training.

DoReMi (DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining, NeurIPS 2023, Google DeepMind + Stanford) is one of the earliest works on automatic pretraining data mixture optimization. Subsequent methods like RegMix, DoGE, and Online Data Mixing all respond to or build upon the framework it introduced. The core idea: run Group DRO (Group Distributionally Robust Optimization, an online algorithm from Sagawa et al. 2020 that minimizes worst-group loss) on a 280M proxy model to dynamically adjust domain weights, average the weights across the training trajectory, then use those weights to train an 8B model. The total cost of the mixture search (training both a reference and a proxy model at 280M) is roughly 8% of the 8B main model’s compute, yet the main model’s downstream accuracy improves by about 6.5 points and reaches the baseline’s final accuracy in roughly 1/2.6 the training steps.

MAI-Thinking-1 (MAI-Thinking-1: Building a Hill-Climbing Machine, 2026, Microsoft AI) is a reasoning model trained from scratch by Microsoft, with a 35B active / 1T total parameter MoE architecture, pre-trained on 30T tokens. The data section of this technical report is remarkably thorough, covering collection, cleaning, mixture optimization, and mid-training data strategy across nearly every decision worth documenting in a full pre-training data pipeline.

Three design principles run through the entire report:

• Capabilities should be learned, not inherited: no distillation, since imitated capabilities lack the steerability and robustness needed for long RL climbs.
• Simplicity is sustainable: simple, scalable recipes; clean, trustworthy data; transparent infrastructure.
• Scientific rigor avoids shortcuts: every decision must be validated through scaling ladders, ablations, and evaluations.

This post focuses on the data collection, cleaning, mixture selection, and mid-training data strategy for the pre-training base model MAI-Base-1.

In LLM training, the pretraining data mixture used to be treated as core know-how, set by gut feel: upsample Wikipedia because it’s clean, downweight Common Crawl because it’s noisy. Once data sources grow from a handful to a few hundred and total tokens cross the trillion line, that approach breaks down. Most existing automatic methods (DoReMi, DoGE, Online Data Mixing, etc.) train a not-so-small proxy model and adjust weights based on training dynamics, with the proxy itself often burning hundreds of billions of tokens.

RegMix (Data Mixture as Regression for Language Model Pre-training, ICLR 2025) proposes a different route: train a few hundred 1M-parameter proxy models, each on a random mixture; treat (mixture, validation loss) as regression data, fit a LightGBM, then search the mixture space for the optimum. 512 1M models trained on 1B tokens each consume roughly 2% the FLOPs of a single 1B model, yet they pick the best mixture out of 64 candidate 1B/25B-token models accurately. Pushed to 7B/100B tokens, the chosen mixture beats the Pile’s hand-tuned mixture by about 2 points averaged across 13 downstream tasks. On the Pile, the single-task gap between mixtures can reach 14.6 points, which the paper uses to make the case that picking the wrong mixture is expensive.

Neural scaling laws $L \propto N^{-\alpha}$ hold across model families, datasets, and tasks with strikingly consistent exponents. But why this power law, and where does the exponent come from? The mechanism has stayed unclear. Most existing explanations follow the same template: assume that feature/skill importance in the data follows a power-law distribution, and the loss naturally becomes a power law. The blame goes to the data.

NeurIPS 2025 Best Paper Runner-up Superposition Yields Robust Neural Scaling traces the answer back to geometry. The hidden dimension $m$ of an LLM is much smaller than the number of features $n$ it must represent. The $n$ feature vectors are forced into an $m$-dimensional space, and the typical interference between them scales as $1/m$, which means the loss also drops as $1/m$. The paper adds a knob to Anthropic’s toy model that independently tunes the strength of superposition. As long as superposition is strong enough, the $1/m$ law holds robustly, almost regardless of the feature frequency distribution. In short: the exponent and robustness of scaling laws come from the geometry of the $m$-dimensional sphere, not from the data.

A safety-aligned model from SFT/RLHF can be turned back into a harmful one with a few hundred examples; even a benign customer-service SFT round will quietly drop the refusal rate. Why is alignment this brittle?

One of the ACL 2025 best papers, Language Models Resist Alignment: Evidence From Data Compression, attributes it to elasticity: alignment fine-tuning does not really rewrite the model’s internal representations, it only nudges the output distribution away from the pre-training distribution temporarily. Under a reverse fine-tune, the model rebounds toward pre-training much faster than it moved toward alignment. Treating an LM as a lossless compressor, the paper derives that the change in compression rate is inversely proportional to dataset size; alignment data is orders of magnitude smaller than the pre-training corpus, so the constraint is naturally weaker.

After DeepSeek-R1, RLVR (Reinforcement Learning with Verifiable Rewards) has more or less become the standard recipe for “growing reasoning ability out of a small model on its own,” and the ever-rising pass@1 curves make it easy to feel that RL keeps teaching the model new tricks. The Tsinghua LeapLab and SJTU paper Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model? (NeurIPS 2025 Oral; also ICML 2025 AI4MATH workshop best paper) asks a direct question: is RLVR adding new reasoning ability to the base model, or is it just sampling its existing reasoning paths more reliably?

The authors’ core claim: with a large enough k (128 to 1024), the pass@k of an RLVR-trained model beats base at small k but is consistently overtaken by base at large k. Further coverage and perplexity analyses show that the reasoning paths RLVR outputs already live inside the base model’s sampling distribution. RLVR only sharpens the distribution onto the questions base can already solve, without introducing problems base cannot. Only distillation actually expands the set of problems the model can solve.

ANCE (Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval, 2020, Microsoft, ICLR 2021) is an old 2020 paper, but the standard practice of hard negative mining in dense retrieval training basically took shape here, and DPR variants, E5, and BGE all inherit the same framework. The core idea: maintain an ANN index throughout training, sample the negatives that the current model finds hardest to distinguish from the whole corpus, and refresh the index asynchronously on a fixed schedule.

The paper has two contributions. First, an importance-sampling variance analysis showing that the in-batch and BM25 negatives commonly used in dense retrieval training have near-zero gradient norms and are the convergence bottleneck. Second, a concrete recipe for sampling hard negatives from the full corpus via ANN, together with a solution to the engineering bottleneck of “the index has to stay in sync with the model.” On TREC 2019 DL, MS MARCO, NQ, and TQA, a BERT-Siamese backbone trained with ANCE reaches NDCG@10 0.628 (MaxP) on document retrieval, MRR@10 0.330 on passage retrieval, beats DPR on Top-20 Coverage on both NQ and TQA, and improves offline retrieval quality by roughly 14%~15% on an 8B-corpus production setting.

ICML 2025 named 8 Outstanding Papers, and Roll the Dice & Look Before You Leap: Going Beyond the Creative Limits of Next-Token Prediction is one of them. The authors are from CMU and Google Research. The paper asks a blunt question: why do LLMs keep producing bland, repetitive output on open-ended tasks like writing puns, designing Olympiad problems, or brainstorming research ideas?

The authors’ core argument: on these tasks humans first commit to an abstract idea and then generate content around it; next-token prediction (NTP) cannot learn this pattern. To fix it, you first have to swap out the training objective so the model can actually learn that hidden idea, and then move the inference-time randomness from the output side to the input side, so the idea is not torn apart by per-position noise during sampling.

The core idea of NV-Embed (NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models, 2024, NVIDIA, ICLR 2025): start directly from Mistral 7B, drop the causal attention mask, attach a latent attention layer on top of the last hidden state for pooling, then run two-stage contrastive instruction tuning, retrieval data with in-batch negatives first, then a mix with non-retrieval data and in-batch negatives turned off. On the 56-task MTEB, NV-Embed-v1 averages 69.32, and v2 pushes the score to 72.31 with hard-negative mining, synthetic data, and example-based multi-class labeling, taking the No.1 spot on MTEB in May and August 2024 respectively.

Shallow Safety Alignment (Safety Alignment Should Be Made More Than Just a Few Tokens Deep, 2024, Princeton & Google DeepMind, ICLR 2025 Outstanding Paper) makes one core argument: the safety alignment of current LLMs primarily modifies the generative distribution of only the first few output tokens. The paper names this phenomenon shallow safety alignment, and shows it explains the common cause behind multiple jailbreaks: prefilling, adversarial suffix attacks, decoding-parameter attacks, and fine-tuning attacks.

The authors locate the phenomenon through three experiments — per-token KL divergence, prefix-prefilling, and per-token fine-tuning dynamics — and verify that “deepening alignment” mitigates multiple attacks via a simple data augmentation and a constrained fine-tuning loss. Experiments are conducted on Llama-2-7B-Chat and Gemma-1.1-7B-IT, evaluated on HEx-PHI, AdvBench, and MaliciousInstruct.

The core idea of Gated Attention (Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free, 2025, Qwen Team, NeurIPS 2025 Best Paper): insert a head-specific, elementwise sigmoid gate after the Scaled Dot-Product Attention output. Trained on 1.7B dense and 15B MoE (A2.54B activated) models for 3.5T tokens each, the method reduces PPL by roughly 0.05–0.27 (depending on model and setting), substantially suppresses training loss spikes, improves long-context extrapolation, and dramatically mitigates the attention sink (BOS token attention drops from 46.7% to 4.8%).

The authors systematically compare 5 candidate positions and several gate forms (granularity, parameter sharing, multiplicative/additive, activation function), totaling 30 variants. The above configuration is the best across all settings.

The core idea of MRL (Matryoshka Representation Learning, 2022): during training, force the first $m$ dimensions of the same $d$-dimensional vector (with $m$ chosen on a logarithmic ladder ${8, 16, 32, \dots, d}$) to independently carry the classification loss, producing a coarse-to-fine nested representation. At inference, just take the first $m$ dimensions according to the compute budget; accuracy matches a separately trained $m$-dim model.

GritLM (Generative Representational Instruction Tuning, 2024) makes one core proposal: a single LLM handles both embedding and generation, distinguishing the two streams via the instruction format, trained jointly with a contrastive loss and a language modeling loss summed together. Where HyDE showed that “LLM handles relevance, encoder handles similarity” can be decoupled, GritLM goes the other way and merges them back into one model.

HyDE (Precise Zero-Shot Dense Retrieval without Relevance Labels, 2022) makes one core proposal: in zero-shot retrieval, instead of asking an unsupervised encoder to model query-document relevance directly, first let an LLM generate a hypothetical document for the query, then encode that document and use it for retrieval. The training objective of LLM2Vec-Gen is essentially this two-step pipeline internalized into the encoder.

Last year’s LLM2Vec showed that decoder-only LLMs can be turned into solid embedding models. This year the same McGill NLP group released LLM2Vec-Gen with the opposite intuition: an embedding should represent not the query itself, but the LLM’s potential response to that query.

A concrete example: a user types “how to commit fraud”. A standard embedder encodes the query and retrieves fraud-related passages. LLM2Vec-Gen encodes the response the model would have produced, “I’m sorry, but I can’t assist with that”, and retrieves refusal-style text instead. Safety alignment isn’t retrained at the embedding stage; it’s inherited directly from the generation side.

Another ICLR 2026 outstanding paper, from Microsoft Research and Salesforce Research. The title is direct: LLMs Get Lost In Multi-Turn Conversation. The conclusion is also direct: take any single-turn task, split the instruction across multiple turns, and 15 frontier LLMs (including GPT-4.1, Gemini 2.5 Pro, Claude 3.7 Sonnet, o3, DeepSeek-R1) all show degradation, averaging -39%. The paper specifically emphasizes that the unreliability increase has roughly the same magnitude across all models, regardless of scale, reasoning capability, or open/closed weight.

What’s more interesting is the breakdown: aptitude only drops about 15%, while unreliability surges 112%. In other words, models in multi-turn settings aren’t dumber, they’re inconsistent. The same task run ten times produces best-case and worst-case results separated by 50 points.