Skip to content

Translation in progress Some entries still include text in the original language.

Oct 12 · Monday

All papers

Found 24 papers

Another 265papers do not have category tags yet and are not included in filtered results.

  1. DefensesarXiv 2610.12602

    Self-Preservation in Language Models

    Proposes Task Bank self-replay to mitigate held-out forgetting in continual instruction tuning

    Published
    Evaluated models
    Qwen3-0.6B, Qwen3-1.7B, Qwen3-4B and others4
    AbstractThe authors claim self-replay can reduce forgetting outside the list, and that on the 4B model new task information helps with target selection, while smaller models do not.

    The authors study how language models, when learning new tasks, can use their own knowledge for broader self-preservation: given an incomplete list and forgetting that extends beyond the list, can the model generate replay and help choose targets.

    Paper analysisRead analysis
  2. DefensesarXiv 2610.12432

    FAITH: A feasibility-aware safety filtering reinforcement learning framework for high-dimensional systems

    Proposes the FAITH safe filtering reinforcement learning framework, training high-dimensional systems to optimize task reward under constraints

    Published
    AbstractFAITH separates safety filtering from reward optimization.

    FAITH is a model-free safe filtering reinforcement learning framework for high-dimensional systems: safety correction and task reward reside in separate modules.

    Paper analysisRead analysis
  3. DefensesarXiv 2610.10833

    研究:Agent 准时停机不等于有效使用时间预算

    用时钟 harness 和预算塑形 GRPO 改善 Agent 的按时返回

    Published
    Setting
    AI Agent
    Evaluated models
    Qwen3.6-27B, Qwen3-4B, Qwen3.5-9B and others4
    Abstract作者比较提示词、harness 与 GRPO:按时性可改善,但额外时间仍未变成更好解。

    作者研究小规模 LLM 智能体在显式 wall-clock 预算下能否按时返回,并把更多时间换成更好的解。

    Paper analysisRead analysis
  4. DefensesarXiv 2610.07935

    SIGMA:让模型依据 Model Spec 自我改进安全对齐

    提出 SIGMA,让模型依据 Model Spec 自造对齐数据并改进安全对齐

    Published
    Setting
    AI Agent
    Evaluated models
    Qwen3.6-27B
    AbstractSIGMA 用 Model Spec 自造监督,单轮训练可迁到多项智能体安全指标。

    SIGMA 让 Qwen3.6-27B 只凭 Model Spec 和高层领域,自己生产对齐任务、评分细则和奖励。

    Paper analysisRead analysis
  5. DefensesarXiv 2610.04616

    PerturBot 用扰动训练打破视觉-语言-动作模型的模态捷径

    提出 Perturbot 扰动训练,打破 VLA 的模态捷径

    Published
    Evaluated models
    π0.5
    AbstractPerturbot 改训练数据以削弱三类模态捷径,GroundFscore 用来看成功是否来自任务证据。

    Perturbot 是一套不改推理的 VLA 训练期数据干预,用来削弱模态捷径,并用离线分数区分“任务变好”和“靠捷径变好”。

    Paper analysisRead analysis
  6. DefensesarXiv 2610.04299

    R-Quest:用问题有效性与新颖性反馈维持推理模型自我进化

    提出 R-Quest,用有效性与新颖性反馈维持推理模型自我进化

    Published
    Evaluated models
    Qwen3-4B-Base, OctoThinker-3B-Hybrid-Base
    AbstractR-Quest 用有效性与新颖性反馈维持自进化。

    R-Quest 处理的是推理模型自进化中的题目质量:questioner 与 solver 用 GRPO 交替从自生成题目学习时,多轮之后平均成绩会下降。

    Paper analysisRead analysis
  7. DefensesarXiv 2610.02126

    Local Support Learning:无需旧数据缓解 LLM 灾难性遗忘

    提出 Local Support Learning,把微调适配器限制在当前数据支撑以缓解遗忘

    Published
    Evaluated models
    Qwen2.5-7B-Instruct, Qwen2.5-3B-Instruct, Qwen2.5-1.5B-Instruct
    AbstractLSL 用 GMM 门把适配器限制在当前数据支撑内,以减轻多阶段遗忘。

    LSL 是不访问旧数据、在后训练中保持先前能力的框架。

    Paper analysisRead analysis
  8. DefensesarXiv 2609.36820

    CorrGRPO:面向多奖励学习的相关性归一化 GRPO

    提出 CorrGRPO,用 Pearson 相关归一化多奖励优势

    Published
    Setting
    AI Agent
    Evaluated models
    Qwen2.5-Coder-0.5B-Instruct, Qwen2.5-Coder-1.5B-Instruct, Qwen2.5-Coder-3B-Instruct and others8
    AbstractCorrGRPO 用 Pearson 相关归一化多奖励 GRPO 优势,并在三个域报告改进。

    CorrGRPO 是 GRPO 的多奖励变体:组内优势的分子仍是中心化总奖励,分母改为 Pearson 相关之和,避免大尺度奖励主导归一化。

    Paper analysisRead analysis
  9. DefensesarXiv 2609.34857

    CREDO:用可微读出替代文本采样校准推理模型置信度

    提出 CREDO,用保留 token 对的可微读出校准推理模型置信度

    Published
    Evaluated models
    Qwen3-8B, Qwen3-1.7B, Qwen3-4B
    AbstractCREDO 用可微两 token 读出替代口头置信,在数学和代码上同时拉高准确率与校准。

    CREDO 把 RLVR 里的置信从“采样一个数字”改成“读一对保留 token 的相对概率”,并用可微回归和差异加权同时拉准确率与校准。。

    Paper analysisRead analysis
  10. AttacksarXiv 2609.27124

    Fed-ADR:用恶意编排服务器协同客户端梯度操纵攻击联邦学习

    提出 Fed-ADR,用编排服务器协同操纵梯度攻破联邦学习并检测恢复

    Published
    Attacker
    Gray-box
    Evaluated models
    CNN(MNIST:2 Conv + 3 FC), CNN(Fashion-MNIST:6 Conv + 1 FC), ResNet18(CIFAR-10)
    AbstractFed-ADR 用 gray-box 编排攻击绕过多种聚合防御,再用 Hessian 一致性检测和窗口内恢复把准确率拉回。

    Fed-ADR 是一套面向联邦学习的攻击、检测与恢复框架:编排服务器改写合谋客户端的梯度,使参数服务器的现有防御把投毒更新当成良性,同时在发现后用历史梯度把全局模型原地修回来。

    Paper analysisRead analysis
  11. DefensesarXiv 2609.21561

    自蒸馏中的排斥与吸引教师:将正确性与行为分离

    提出对比自蒸馏,分离正确性与行为偏移

    Published
    Evaluated models
    Qwen3-4B, Qwen3-4B-Instruct-2507
    Abstract隔离自蒸馏后,对比正确与错误解教师可抵消行为偏移并稳定提升推理。

    把自蒸馏从 GRPO 里拆开后,用靠近正确解教师、远离错误解教师的对比信号,把正确性和行为分开。。

    Paper analysisRead analysis
  12. DefensesarXiv 2609.04714

    无需拒答的拒答:减少语言模型误拒的安全微调响应结构分析

    提出 Rationale-Only 安全微调,去掉拒答声明以降低误拒

    Published
    Evaluated models
    Llama-3.1-8B, Mistral-7B-v0.3, Gemma-2-9B and others8
    Abstract去掉套话拒答声明、只训理由,可降低假拒答,有害查询上的服从率仍低。

    这是一项针对安全微调数据的结构分析:假拒答被作者追溯到回答里的套话式拒答声明,而不是只靠推理时补救。

    Paper analysisRead analysis