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What and When to Distill: Selective Hindsight Distillation for Multi-Turn Agents

BACKGROUND
4 SIGNALSFIRST DETECTED 20 May 2026
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How the story developed
17 Oct
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15 Oct
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11 Jun
20 May
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Other feeds and industry voices
13 May
Adaptive Teacher Exposure for Self-Distillation in LLM Reasoning

arXiv:2605.11458v1 Announce Type: new Abstract: On-policy self-distillation has become a strong recipe for LLM reasoning, where a privileged teacher supervises the student's own

REPORT · WHERE · ArXiv CS.AIOPEN STORY
9 Apr
Scaling Coding Agents via Atomic Skills

arXiv:2604.05013v1 Announce Type: cross Abstract: Current LLM coding agents are predominantly trained on composite benchmarks (e.g., bug fixing), which often leads to

REPORT · HOW · ArXiv CS.AIOPEN STORY
25 Sept
From Self-Distillation to Self-Practice: Privileged Information for Multi-Turn Agents

arXiv:2609.29051v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) has become a popular recipe for post-training LLM agents. It supervises the agent model at the

REPORT · WHAT · ArXiv CS.AIOPEN STORY
13 May
Hindsight Hint Distillation: Scaffolded Reasoning for SWE Agents from CoT-free Answers

arXiv:2605.11556v1 Announce Type: new Abstract: Solving complex long-horizon tasks requires strong planning and reasoning capabilities. Although datasets with explicit

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6 May
Segment-Aligned Policy Optimization for Multi-Modal Reasoning

arXiv:2605.01327v1 Announce Type: new Abstract: Existing reinforcement learning approaches for Large Language Models typically perform policy optimization at the granularity of

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20 Mar
Memento-Skills: Let Agents Design Agents

arXiv:2603.18743v1 Announce Type: new Abstract: We introduce \emph{Memento-Skills}, a generalist, continually-learnable LLM agent system that functions as an \emph{agent-designing

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14 Apr
MEMENTO: Teaching LLMs to Manage Their Own Context

arXiv:2604.09852v1 Announce Type: new Abstract: Reasoning models think in long, unstructured streams with no mechanism for compressing or organizing their own intermediate state.

REPORT · HOW · ArXiv CS.AIOPEN STORY
12 May
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria

arXiv:2605.08354v1 Announce Type: new Abstract: Aligning multimodal generative models with human preferences demands reward signals that respect the compositional,

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