Skip to content
Papers.

Deeper is Not Always Better: Mitigating the Alignment Tax via Confident Layer Decoding research paper by Alibaba (Qwen), 2026

Alibaba (Qwen) · Jun 20, 2026 · Alignment and safety · 0 citations · 27 upvotes · unverified

Read on arXiv

What it shows

Autoregressive generation in large language models traditionally uses the final layer for token prediction, but a new decoding strategy dynamically selects more reliable intermediate layers based on entropy-guided search, improving reasoning performance with minimal computational overhead.

UnverifiedHugging Face's summary; not yet checked by hand.

More from Alibaba (Qwen)

All 61
PaperCitations
HappyWorld-BenchEvaluating world models requires assessing both the quality of the worlds they generate and their consistency and responsiveness under exploration, interaction, and modification.Agents and evaluation · Sep 2026 · Unverified0
One to More, More to One: Category-Aware Iterative Expert Training for Software Engineering AgentsRepository-level software engineering (SWE) comprises heterogeneous task categories, whose progress under pooled agentic reinforcement learning can be uneven: gains in some categories coincide with regressions in...Training and scaling · Sep 2026 · Unverified0
OmniVChat: Synthesizing, Benchmarking, and Training for Native Audio-Visual DialogueWe define OmniVChat (Omni Video Chat) as the task of native audio-visual dialogue between a user and an omni model.Multimodal and robotics · Sep 2026 · Unverified0
RecreationWorld: Scalable and Verifiable Environments for Hybrid Computer-Use AgentsComputer-use agents (CUAs) have advanced along two separate lines: graphical interaction and software development through code and the command line.Agents and evaluation · Sep 2026 · Unverified0
CORE: Improving Compositional Reasoning in MLLM Embedding via Reranker DistillationCORE distills compositional ranking judgments from a cross-attentive reranker into an embedding model via synthesized multi-level candidates and a Rank-KL objective, improving compositional retrieval without degrading standard performance.Reasoning · Sep 2026 · Unverified0
Terminal-Universe: Turning Agent Trajectories into Scalable Terminal EnvironmentsTerminal-Universe reconstructs executable workspaces from agent trajectories to synthesize diverse training tasks and improves post-training performance through supervised fine-tuning.Agents and evaluation · Sep 2026 · Unverified1
Qwen-Drive-1.0: An Initial Step towards a Vision-Language Foundation Model for Autonomous DrivingQwen-Drive-1.0 is a vision-language foundation model for autonomous driving that unifies 3D perception, visual question answering, and motion planning via shared representations and staged training.Multimodal and robotics · Aug 2026 · Unverified3
On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training StabilityQwen3.8-Flash-Next: a 125B mixture-of-experts model with 6B active that nearly matches its 397B predecessor at 1/9 the training compute.Architectures · Aug 20266

Other alignment and safety papers

Topic
PaperCitations
Training language models to follow instructions with human feedbackInstructGPT: fine-tuning on human feedback made a 1.3B model preferred over the 175B GPT-3.OpenAI · Mar 202224.3k
Direct Preference Optimization: Your Language Model is Secretly a Reward ModelDPO aligns a model to human preferences with a simple classification loss, no reward model or RL loop.Stanford University · May 202310.6k
Constitutional AI: Harmlessness from AI FeedbackConstitutional AI trains a harmless assistant from AI feedback guided by a short list of written principles, not human harm labels.Anthropic · Dec 20223,721
Sleeper Agents: Training Deceptive LLMs that Persist Through Safety TrainingModels trained with a hidden backdoor kept their deceptive behaviour through standard safety training.Anthropic · Jan 2024578
Alignment faking in large language modelsClaude 3 Opus sometimes went along with a training goal it disagreed with, to avoid being changed, without being told to.Anthropic · Dec 2024343
Why Language Models HallucinateModels hallucinate because training and benchmarks reward confident guessing over saying they do not know.OpenAI · Sep 2025331
About this paper
Authors
Xuanming Zhang, Sining Zhoubian, Yuxuan Chen and 8 more
arXiv
2606.21906 · PDF
Venue
arXiv.org
Citations
0, 0 influential · Semantic Scholar
Upvotes
27 · Hugging Face
Code
github.com/QwenLM/Confident-Decoding
Lab
Alibaba (Qwen) · on Companies · on Quarterly

Changes

What changed
Influential citationsfirst count: 0Sep 25, 2026
Citationsfirst count: 0Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

New papers by email

Monday afternoons, only in weeks with new papers from the labs.

Double opt-in. Unsubscribe any time.