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arxiv: 2307.09288 · v2 · pith:X3D47VQTnew · submitted 2023-07-18 · 💻 cs.CL · cs.AI

Llama 2: Open Foundation and Fine-Tuned Chat Models

Hugo Touvron , Louis Martin , Kevin Stone , Peter Albert , Amjad Almahairi , Yasmine Babaei , Nikolay Bashlykov , Soumya Batra
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Pith reviewed 2026-05-24 07:56 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords Llama 2large language modelsfine-tuningchat modelsdialoguesafetyopen sourcepretrained models
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The pith

Llama 2 releases pretrained and fine-tuned models from 7B to 70B parameters whose chat versions outperform open-source alternatives on dialogue benchmarks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops and publicly releases Llama 2, a set of large language models in several sizes, along with fine-tuned chat versions called Llama 2-Chat. These chat models are built specifically for conversation and exceed the performance of other openly available chat systems across the benchmarks the authors report. Human ratings on helpfulness and safety lead the authors to conclude that the models could replace closed-source chat systems in many settings. The work also spells out the fine-tuning steps and safety measures so that others can replicate and extend the approach.

Core claim

Llama 2 consists of pretrained foundation models and corresponding Llama 2-Chat variants ranging from 7 billion to 70 billion parameters; the chat variants are optimized for dialogue, outperform open-source chat models on most evaluated benchmarks, and receive human ratings for helpfulness and safety that suggest they may serve as substitutes for closed-source models.

What carries the argument

The fine-tuning pipeline and accompanying safety mitigations applied to the base pretrained models to produce dialogue-specialized Llama 2-Chat versions.

If this is right

  • Open models can reach performance levels previously associated only with proprietary systems on dialogue tasks.
  • Public release of both weights and training details allows the community to reproduce and improve safety techniques.
  • Models at multiple scales give practitioners choices between compute cost and capability for chat applications.
  • Detailed documentation of the safety stage reduces the barrier for responsible further development of similar systems.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Wider availability of competitive open chat models could lower barriers for researchers and developers working on conversational AI.
  • Future work could test whether the same fine-tuning recipe transfers to non-English dialogue or to specialized domains.
  • Independent audits of the released models would provide external confirmation of the safety claims.
  • The scaling pattern across 7B–70B sizes offers a concrete reference point for predicting performance at intermediate sizes.

Load-bearing premise

The reported benchmark scores and human ratings on helpfulness and safety accurately reflect real-world dialogue performance without selection bias or evaluator effects.

What would settle it

A controlled blind evaluation in which independent raters consistently judge Llama 2-Chat responses as less helpful or less safe than those from leading closed-source chat models on matched prompts.

Figures

Figures reproduced from arXiv: 2307.09288 by Adina Williams, Alan Schelten, Amjad Almahairi, Andrew Poulton, Angela Fan, Anthony Hartshorn, Artem Korenev, Aurelien Rodriguez, Binh Tang, Brian Fuller, Cristian Canton Ferrer, Cynthia Gao, Dan Bikel, David Esiobu, Diana Liskovich, Eric Michael Smith, Guillem Cucurull, Hakan Inan, Hugo Touvron, Igor Molybog, Iliyan Zarov, Isabel Kloumann, Jenya Lee, Jeremy Fu, Jeremy Reizenstein, Jian Xiang Kuan, Jude Fernandes, Kalyan Saladi, Kevin Stone, Louis Martin, Lukas Blecher, Madian Khabsa, Marcin Kardas, Marie-Anne Lachaux, Melanie Kambadur, Moya Chen, Naman Goyal, Nikolay Bashlykov, Peter Albert, Prajjwal Bhargava, Punit Singh Koura, Pushkar Mishra, Puxin Xu, Ranjan Subramanian, Rashi Rungta, Robert Stojnic, Ross Taylor, Ruan Silva, Rui Hou, Saghar Hosseini, Sergey Edunov, Sharan Narang, Shruti Bhosale, Soumya Batra, Thibaut Lavril, Thomas Scialom, Todor Mihaylov, Vedanuj Goswami, Viktor Kerkez, Wenyin Fu, Xavier Martinet, Xiaoqing Ellen Tan, Yasmine Babaei, Yinghai Lu, Yixin Nie, Yuchen Zhang, Yuning Mao, Zheng Yan.

Figure 1
Figure 1. Figure 1: Helpfulness human evaluation results for Llama 2-Chat compared to other open-source and closed-source models. Human raters compared model generations on ~4k prompts consisting of both single and multi-turn prompts. The 95% confidence intervals for this evaluation are between 1% and 2%. More details in Section 3.4.2. While reviewing these results, it is important to note that human evaluations can be noisy … view at source ↗
Figure 3
Figure 3. Figure 3: Safety human evaluation results for Llama 2-Chat compared to other open-source and closed￾source models. Human raters judged model generations for safety violations across ~2,000 adversarial prompts consisting of both single and multi-turn prompts. More details can be found in Section 4.4. It is important to caveat these safety results with the inherent bias of LLM evaluations due to limitations of the pro… view at source ↗
Figure 4
Figure 4. Figure 4: Training of Llama 2-Chat: This process begins with the pretraining of Llama 2 using publicly available online sources. Following this, we create an initial version of Llama 2-Chat through the application of supervised fine-tuning. Subsequently, the model is iteratively refined using Reinforcement Learning with Human Feedback (RLHF) methodologies, specifically through rejection sampling and Proximal Policy … view at source ↗
Figure 5
Figure 5. Figure 5: Training Loss for Llama 2 models. We compare the training loss of the Llama 2 family of models. We observe that after pretraining on 2T Tokens, the models still did not show any sign of saturation. Tokenizer. We use the same tokenizer as Llama 1; it employs a bytepair encoding (BPE) algorithm (Sennrich et al., 2016) using the implementation from SentencePiece (Kudo and Richardson, 2018). As with Llama 1, w… view at source ↗
Figure 6
Figure 6. Figure 6: Scaling trends for the reward model. More data and a larger-size model generally improve accuracy, and it appears that our models have not yet saturated from learning on the training data. The fact that helpfulness and safety performed the best on their own domain is potentially due to the tension between the two objectives (i.e., being as helpful as possible versus refusing unsafe prompts when necessary),… view at source ↗
Figure 7
Figure 7. Figure 7: Max and median reward among N samples, N ∈ [1, . . . , 100] averaged over our training set of prompts. The delta between max and median can be interpreted as potential gain with Rejection Sampling. the highest reward score is considered the new gold standard. Similar to Scialom et al. (2020a), we then fine-tune our model on the new set of ranked samples, reinforcing the reward. The two RL algorithms mainly… view at source ↗
Figure 8
Figure 8. Figure 8: RLHF impact of the temperature when sampling N outputs and scoring them with a reward model. Rejection Sampling. We perform rejection sampling only with our largest 70B Llama 2-Chat. All smaller models are fine-tuned on rejection sampled data from the larger model, thus distilling the large-model capabilities into the smaller ones. We leave further analysis of the effect of this distillation for future wor… view at source ↗
Figure 9
Figure 9. Figure 9: Issues with multi-turn memory (left) can be improved with GAtt (right). We train for between 200 and 400 iterations for all our models, and use evaluations on held-out prompts for early stopping. Each iteration of PPO on the 70B model takes on average ≈ 330 seconds. To train quickly with large batch sizes, we use FSDP (Zhao et al., 2023). This was effective when using O(1) forward or backward passes, but c… view at source ↗
Figure 10
Figure 10. Figure 10: Attention visualization for a dialogue with and without GAtt [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Evolution of Llama 2-Chat. We show the evolution after multiple iterations fine-tuning for the win-rate % of Llama 2-Chat compared to ChatGPT. Left: the judge is our reward model, which may favor our model, and right, the judge is GPT-4, which should be more neutral. on diverse open-source Reward Modeling datasets. We have not yet observed any such divergence, and hypothesize that iterative model updates … view at source ↗
Figure 12
Figure 12. Figure 12: Human evaluation results for Llama 2-Chat models compared to open- and closed-source models across ~4,000 helpfulness prompts with three raters per prompt. The largest Llama 2-Chat model is competitive with ChatGPT. Llama 2-Chat 70B model has a win rate of 36% and a tie rate of 31.5% relative to ChatGPT. Llama 2-Chat 70B model outperforms PaLM-bison chat model by a large percentage on our prompt set. More… view at source ↗
Figure 13
Figure 13. Figure 13: Pretraining data toxicity. To allow for better downstream generalization, we chose not to scrub toxic data from pretraining. The HateBERT classifier assigns a toxicity likelihood of 0.5 or higher to about 0.2% of documents in our pretraining corpus. Data Toxicity. We measure the prevalence of toxicity in the English-language portion of the pretraining corpus using a HateBERT classifier fine-tuned on the T… view at source ↗
Figure 14
Figure 14. Figure 14: Impact of safety RLHF measured by reward model score distributions. [PITH_FULL_IMAGE:figures/full_fig_p025_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Safety data scaling trends. Left: as we increase the amount of safety data in model training, the mean safety RM score improves significantly while the helpfulness counterpart remains relatively stable. Right: the left tail of safety RM scores (i.e., most unsafe responses) gradually disappears with the addition of more safety training data. Measure of False Refusal. Even though we do not see overall regre… view at source ↗
Figure 16
Figure 16. Figure 16: Context distillation analysis. Left: Distribution of safety RM scores from the base model, when adding a generic preprompt, and when adding a preprompt based on the risk category with tailored answer template. While a generic preprompt increases safety RM scores, a preprompt with tailored answer template helps even more. Right: Context distillation increases the RM score significantly for samples that ini… view at source ↗
Figure 17
Figure 17. Figure 17: Overall safety measures. Left: Llama 2-Chat has low violation percentage overall across model sizes. Right: Llama 2-Chat has high safety and helpfulness mean rating overall across model sizes. It is important to note that these results are subject to limitations of the prompt set, subjectivity of the review guidelines, and subjectivity of individual raters. • 1 - Severe safety violations We consider a rat… view at source ↗
Figure 18
Figure 18. Figure 18: Single-turn and multi-turn violation percentage. [PITH_FULL_IMAGE:figures/full_fig_p030_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Violation percentage per risk category. Note: these results should be interpreted carefully due to limitations of the prompt set, subjectivity of the review guidelines, content standards, and individual raters. In [PITH_FULL_IMAGE:figures/full_fig_p031_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Distribution shift for progressive versions of [PITH_FULL_IMAGE:figures/full_fig_p032_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: RLHF learns to adapt the temperature with regard to the type of prompt. [PITH_FULL_IMAGE:figures/full_fig_p033_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Time awareness — illustration of our model generalizing the notion of time, with 1,000 SFT time-focused data. Llama 2-Chat Temporal Perception Our model showcased impressive generalization ability, as shown in [PITH_FULL_IMAGE:figures/full_fig_p033_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Tool use emergence. Llama 2-Chat is able to understand the tools’s applications, and the API arguments, just through the semantics, despite never having been trained to use tools. The release of OpenAI’s plugins‡‡ has incited substantial discourse within the academic community, igniting questions such as: How can we effectively teach models to utilize tools? or Does the process necessitate a substantial d… view at source ↗
Figure 24
Figure 24. Figure 24: Multi-query variants enable higher throughput with larger batch sizes, and show similar [PITH_FULL_IMAGE:figures/full_fig_p048_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: Distribution of human preference data rating over batches. [PITH_FULL_IMAGE:figures/full_fig_p053_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: Annotation curriculum [PITH_FULL_IMAGE:figures/full_fig_p053_26.png] view at source ↗
Figure 28
Figure 28. Figure 28: We applied first GAtt to Llama 1, which was pretrained with a context length of 2048 tokens and then fine-tuned with 4096 max length. We tested if GAtt works beyond 2048 tokens, and the model arguably managed to understand attributes beyond this window. This promising result indicates that GAtt could be adapted as an efficient technique for long context attention. A.3.6 How Far Can Model-Based Evaluation … view at source ↗
Figure 27
Figure 27. Figure 27: Reward model score distribution shift caused by incorporating preference rating based margin [PITH_FULL_IMAGE:figures/full_fig_p054_27.png] view at source ↗
Figure 28
Figure 28. Figure 28: GAtt zero-shot generalisation. Neither of the two constraints above were present in the training data for GAtt. Yet, they are perfectly fulfilled trough all the turns. 1 2 3 4 5 6 7 Median Response Quality Score 0.0 0.2 0.4 0.6 0.8 1.0 Mean Reward Model Score Helpfulness 1 2 3 4 5 6 7 Median Response Quality Score 0.0 0.2 0.4 0.6 0.8 1.0 Mean Reward Model Score Safety [PITH_FULL_IMAGE:figures/full_fig_p0… view at source ↗
Figure 29
Figure 29. Figure 29: Average reward model score vs model response quality rating (7-point Likert scale) from triple [PITH_FULL_IMAGE:figures/full_fig_p055_29.png] view at source ↗
Figure 30
Figure 30. Figure 30: Impact of system prompt on human evaluation results for ChatGPT (Left). Win rate per category for Llama 2-Chat 70B compared to ChatGPT using system prompts for both models (Right). Evaluation Methodology. For evaluations, the human annotators are presented with a prompt and genera￾tions from two models side-by-side. They are asked to answer the following question: Considering both model responses, which i… view at source ↗
Figure 31
Figure 31. Figure 31: Win rate of Llama 2-Chat versus ChatGPT analyzed by number of turns (Left) in the prompt and word count (Right) for the prompt and generation combined. For the word count plot, we report the win rate for each quintile. The maximum total word count (prompt and generation) is 2432. We do not see any trends in win rate with either word count or turn count. Additional Results. To understand the impact of syst… view at source ↗
Figure 32
Figure 32. Figure 32: Safety and Helpfulness reward model scores on a set of safe [PITH_FULL_IMAGE:figures/full_fig_p064_32.png] view at source ↗
Figure 33
Figure 33. Figure 33: The false model refusal rate increases with the percentage of safety data. Left [PITH_FULL_IMAGE:figures/full_fig_p067_33.png] view at source ↗
read the original abstract

In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript introduces the Llama 2 family of pretrained foundation models (7B–70B parameters) and their fine-tuned chat variants (Llama 2-Chat). It claims that the chat models outperform other open-source chat models on most tested benchmarks and, based on human evaluations of helpfulness and safety, may serve as a suitable substitute for closed-source models in dialogue use cases. The work also provides a detailed account of the RLHF fine-tuning pipeline and safety improvements to support community reuse and responsible LLM development.

Significance. If the benchmark and human-evaluation claims hold, the release of competitive open-weight models at this scale, together with the documented fine-tuning and safety procedures, would constitute a substantial contribution by enabling broader access to high-performing dialogue systems and providing a concrete reference for safety tuning practices.

major comments (3)
  1. [Abstract] Abstract: the central claim that Llama 2-Chat 'may be a suitable substitute for closed-source models' is explicitly conditioned on the human evaluations for helpfulness and safety; however, the manuscript supplies no information on prompt sampling strategy, blinding, rating-scale definitions, inter-annotator agreement statistics, or statistical tests for the reported preference rates. This absence directly affects the ability to rule out selection effects or annotator bias and is therefore load-bearing for the substitute-model conclusion.
  2. [Evaluation sections] Evaluation sections (presumed §5–6): while benchmark results are presented, the paper does not report the exact data splits, number of runs, or variance estimates underlying the 'outperform on most benchmarks' statement, making it impossible to assess whether the observed margins are robust or sensitive to post-hoc selection of test sets.
  3. [Safety tuning description] Safety tuning description (presumed §4): the RLHF pipeline is outlined at a high level, yet no quantitative ablation is given showing the incremental contribution of each safety stage (e.g., rejection sampling vs. PPO) to the final human safety ratings; without such controls the attribution of the reported safety improvements remains under-specified.
minor comments (2)
  1. [Throughout] Notation for model sizes (7B, 13B, 70B) is used inconsistently with respect to whether parameter counts are exact or approximate; a single clarifying sentence would remove ambiguity.
  2. [Benchmark tables] Several benchmark tables lack explicit citation of the original evaluation protocols or licenses under which the test sets are used; adding these references would improve reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major point below and indicate the revisions we will make to improve transparency and rigor.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that Llama 2-Chat 'may be a suitable substitute for closed-source models' is explicitly conditioned on the human evaluations for helpfulness and safety; however, the manuscript supplies no information on prompt sampling strategy, blinding, rating-scale definitions, inter-annotator agreement statistics, or statistical tests for the reported preference rates. This absence directly affects the ability to rule out selection effects or annotator bias and is therefore load-bearing for the substitute-model conclusion.

    Authors: We agree that additional methodological details would strengthen the presentation of the human evaluation results. In the revised manuscript we will expand the relevant evaluation section (and/or add an appendix) to describe the prompt sampling strategy, blinding procedures, rating-scale definitions, inter-annotator agreement statistics, and any statistical tests used for the preference rates. revision: yes

  2. Referee: [Evaluation sections] Evaluation sections (presumed §5–6): while benchmark results are presented, the paper does not report the exact data splits, number of runs, or variance estimates underlying the 'outperform on most benchmarks' statement, making it impossible to assess whether the observed margins are robust or sensitive to post-hoc selection of test sets.

    Authors: We acknowledge the value of reporting these details. The revised version will include explicit information on the data splits employed, the number of runs performed where applicable, and any variance or standard-error estimates to allow readers to evaluate robustness. revision: yes

  3. Referee: [Safety tuning description] Safety tuning description (presumed §4): the RLHF pipeline is outlined at a high level, yet no quantitative ablation is given showing the incremental contribution of each safety stage (e.g., rejection sampling vs. PPO) to the final human safety ratings; without such controls the attribution of the reported safety improvements remains under-specified.

    Authors: The safety section intentionally provides a high-level overview of the overall pipeline. We did not perform quantitative ablations that isolate the contribution of each individual stage. We will clarify the existing description where possible, but cannot add new ablation experiments that were outside the scope of the original study. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical claims rest on external benchmarks

full rationale

The paper is an empirical model-release report describing pretraining, RLHF fine-tuning, and evaluation of Llama 2 models. It contains no mathematical derivations, first-principles predictions, fitted parameters presented as novel outputs, or equations that could reduce to their own inputs. All performance claims are tied to comparisons against external benchmarks and separate human ratings whose protocols are described but not defined in terms of quantities internal to the paper. No self-citation chains, ansatzes, or uniqueness theorems are invoked to justify core results. The central claims therefore remain independent of the paper's own definitions.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an empirical model-release paper; no mathematical free parameters, axioms, or invented entities are required for the central performance claims.

pith-pipeline@v0.9.0 · 5941 in / 1181 out tokens · 29609 ms · 2026-05-24T07:56:42.779579+00:00 · methodology

discussion (0)

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    AgentBench is a new multi-environment benchmark showing commercial LLMs outperform open-source models up to 70B parameters in agent tasks mainly due to better long-term reasoning and instruction following.

  30. Universal and Transferable Adversarial Attacks on Aligned Language Models

    cs.CL 2023-07 accept novelty 8.0

    Gradient and greedy search over token suffixes produces universal, transferable adversarial prompts that elicit objectionable outputs from aligned models including black-box commercial systems.

  31. HERMES: A Multi-Granularity Labeling Substrate for Pre-training Data Mixtures

    cs.LG 2026-07 unverdicted novelty 7.0

    HERMES provides a reusable hierarchical labeling substrate for pre-training data that reveals granularity-specific effects in data mixing rules during model training.

  32. Model Merging as Probabilistic Inference in Fine-Tuning Parameter Space

    cs.LG 2026-07 unverdicted novelty 7.0

    Model merging is cast as PoE inference with EBM experts, revealing Gaussian assumptions in prior work and proposing convergent Cauchy experts that improve empirical performance.

  33. SmoothAgent: Efficient Long-Horizon LLM-Based Agent Serving with Lookahead Context Engineering

    cs.DC 2026-06 unverdicted novelty 7.0

    SmoothAgent introduces lookahead context engineering to eliminate transformation overhead in LLM agents, reducing TTFT by up to 11.9x through proactive KV cache preparation.

  34. Revisiting Parameter Redundancy in Vision-Language-Action Models: Insights from VLM-to-VLA Adaptation

    cs.RO 2026-06 unverdicted novelty 7.0

    VLA models from VLM adaptation can be pruned 12-30% via multi-module joint scheme based on divergence signals while keeping ~90% performance on LIBERO without post-pruning recovery, unlike standard criteria that collapse.

  35. FlexiSLM: A Dynamic and Controllable Frame Rate Spoken Language Model

    cs.SD 2026-06 unverdicted novelty 7.0

    FlexiSLM is the first spoken language model supporting dynamic and controllable frame rates on speech input and output, outperforming fixed-rate 7B models at high quality and enabling faster inference at lower rates l...

  36. Indi-RomCoM: Code-Mixed Benchmark for Evaluating LLMs on Romanized Indic-English Instructions

    cs.CL 2026-06 unverdicted novelty 7.0

    Introduces Indi-RomCoM benchmark for evaluating LLMs on Romanized code-mixed Indic-English instructions across seven tasks, four languages, and three mixing levels.

  37. When One Adapter Speaks for Many: Discovering Low-Rank Redundancy in Continual Fine-Tuning

    cs.LG 2026-06 unverdicted novelty 7.0

    Task-specific LoRA adapters in continual learning exhibit significant low-rank subspace overlap, enabling LiteLoRA's learned gating to reduce active adapters by 20-70% while matching or exceeding prior performance.

  38. Drop-Then-Recovery: How Redundant Are Vision-Language-Action Models?

    cs.RO 2026-06 accept novelty 7.0

    VLA language backbones show high redundancy on manipulation benchmarks, with half the LLM blocks removable and even two blocks sufficient to recover baseline performance after fine-tuning, unlike vision and action pathways.

  39. CBD: API-Only LLM Black-Box Unlearning through Controlled Behavioral Divergence

    cs.LG 2026-06 unverdicted novelty 7.0

    CBD is an API-only black-box unlearning method for LLMs that creates controlled behavioral divergence with auxiliary models and uses a Fisher-matrix-derived discriminative basis to balance forgetting target data with ...

  40. Masked Language Flow Models

    cs.CL 2026-06 unverdicted novelty 7.0

    MLFMs combine masking with continuous flows to scale flow-based language models to reasoning and instruction-following tasks on GSM8K and MT-Bench.

  41. Large Language Model Teaches Visual Students: Cross-Modality Transfer of Fine-Grained Conceptual Knowledge

    cs.CV 2026-06 unverdicted novelty 7.0

    LaViD distills LLM conceptual knowledge to vision models via LLM-generated MCQ soft labels, outperforming vision-language distillation baselines on fine-grained benchmarks while improving robustness on spurious correl...

  42. Structure Before Collapse: Transient semantic geometry in next-token prediction

    cs.LG 2026-06 unverdicted novelty 7.0

    Semantic geometry emerges transiently early in next-token prediction training before collapsing to Neural Collapse symmetry in synthetic settings with latent semantic factors.

  43. Preference Optimization Drives Monoculture in LLM Prediction Markets

    cs.CE 2026-06 unverdicted novelty 7.0

    DPO fine-tuning causes LLM agents to share output distributions with pairwise error correlations of ρ=0.70, reducing ten agents to the effective power of ≈1.4 independent forecasters.

  44. DiT-Reward: Generative Representations for Text-to-Image Reward Modeling

    cs.LG 2026-06 unverdicted novelty 7.0

    DiT-Reward converts pretrained DiT models into reward predictors that outperform HPSv3 on four benchmarks while providing 1.65x inference speedup.

  45. Have You Ever Seen Them? Entity-level Membership Inference through Interrogating Large Language Models

    cs.CL 2026-06 unverdicted novelty 7.0

    Entity-level membership inference determines whether information about a target real-world entity was used in LLM training, using only black-box generated text and achieving AUC up to 0.97 on person entities.

  46. NAC: Neural Action Codec for Vision-Language-Action Models

    cs.RO 2026-06 unverdicted novelty 7.0

    NAC adapts multi-scale RVQGAN audio codecs with kinematic-specific losses to produce ordered action tokens that yield lower reconstruction error and higher task success than prior tokenizers in VLA models.

  47. Through the PRISM: Preference Representation in Intermediate States of Video Diffusion Models

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    PRISM shows video diffusion models inherently encode preference information in noisy latents, achieving SOTA accuracy and enabling noise-robust early-stage sampling with a correlation to generative performance.

  48. Can neurons speak? Semantic narration of vision at single-cell resolution

    q-bio.NC 2026-06 unverdicted novelty 7.0

    NEURRATOR bridges neural spike trains to frozen CLIP patch embeddings via a learned encoder, then uses a multimodal LM and sparse autoencoder to produce validated natural-language narrations of viewed scenes from Neur...

  49. Structural Role Injection in Handlebars-Templated LLM Prompts: Triple-Brace Interpolation, Delimiter Family, and the Limits of HTML Auto-Escaping

    cs.CR 2026-06 conditional novelty 7.0

    Handlebars double-brace escaping neutralizes angle-bracket role delimiters but not colon- or Markdown-based ones, as measured by survival rates and 5760 model trials across four LLMs.

  50. PearlVLA: Progressive Embodied Action-Plan Refinement in Latent Space

    cs.RO 2026-06 unverdicted novelty 7.0

    PearlVLA achieves SOTA on LIBERO by separating VLM representations into visual grounding and an iterative latent plan branch refined via world model queries and RefineNet with process-reward RL.

  51. Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs

    cs.CR 2026-06 unverdicted novelty 7.0

    Poisoning training data reshapes the loss landscape to enable targeted extraction of unseen data from LLMs with high success rates in language and vision-language models.

  52. RATrain: A Resource-Aware Training Runtime for Large Language Models on Bandwidth-Constrained Heterogeneous Supercomputing Platforms

    cs.DC 2026-06 unverdicted novelty 7.0

    RATrain introduces a resource-aware scheduler and MT-3000-specific backend for 1F1B LLM training that achieves 1.35x speedup and 97% scaling efficiency while preserving training correctness.

  53. APEX4: Efficient Pure W4A4 LLM Inference via Intra-SM Compute Rebalancing

    cs.DC 2026-06 conditional novelty 7.0

    APEX4 co-designs pure INT4 GEMM kernels with ρ-aware granularity adaptation to deliver up to 2.09× end-to-end speedup on GPUs with low ρ while keeping LLaMA-2-70B perplexity within 0.63 of FP16.

  54. STAR-KV: Low-Rank KV Cache Compression via Soft Thresholding for Adaptive Rank Control

    cs.LG 2026-06 unverdicted novelty 7.0

    STAR-KV applies differentiable soft thresholding for per-head and per-block adaptive low-rank KV cache compression, combined with hybrid decomposition and low-rank-aware quantization, achieving up to 75% compression a...

  55. DICE: Entropy-Regularized Equilibrium Selection for Stable Multi-Agent LLM Coordination

    cs.LG 2026-06 unverdicted novelty 7.0

    DICE formalizes multi-agent LLM coordination as discounted incomplete-information Markov games and introduces Heterogeneous Quantal Response Equilibrium (HQRE) to achieve unique stable equilibria with bounded regret, ...

  56. From Correctness to Utility: Gain-Based Prefix Evaluation for LLM Reasoning

    cs.CL 2026-06 unverdicted novelty 7.0

    Prefix gain measured via student-model solve-rate improvement is used to train a Prefix Utility Model (PUM) that supplies stronger supervision than correctness-based process rewards for mathematical reasoning.

  57. Many Circuits, One Mechanism: Input Variation and Evaluation Granularity in Circuit Discovery

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  58. SlotGCG: Exploiting the Positional Vulnerability in LLMs for Jailbreak Attacks

    cs.CR 2026-06 unverdicted novelty 7.0

    SlotGCG uses Vulnerable Slot Score (VSS) to identify and target the most vulnerable prompt positions for adversarial token insertion, delivering 14% higher ASR than standard GCG and 42% higher against defenses.

  59. Multilingual Coreference Resolution via Cycle-Consistent Machine Translation

    cs.CL 2026-06 unverdicted novelty 7.0

    A cycle-consistent MT pipeline generates and similarity-weights training data for coreference resolution, producing gains on four low-resource languages and enabling the task where no corpora existed.

  60. Benchmarking Visual State Tracking in Multimodal Video Understanding

    cs.CV 2026-06 unverdicted novelty 7.0

    VSTAT benchmark shows state-of-the-art MLLMs perform far below humans and only modestly above answer-prior baselines on visual state tracking, failing at visual perception despite correct textual reasoning.

Reference graph

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