Reinforcement learning from human feedback

Evidence in the last complete seven days

Source mentions

2

Previous seven days 0

Distinct sources

1

Previous 0

Comparable growth

Not comparable

Ranking appearances

0

Collection observations and undated records are separate from dated publication counts. Observed in 7d：0 · Undated records：0

Daily history · up to 90 days

Bars show dated source mentions. The latest UTC day may be incomplete. Zero mentions and missing collection coverage are different conditions.

Dotted markers indicate days with no record. They are not counted as zero mentions.

Source evidence

Sources in the evidence shown：HF Daily Papers 1 · arXiv 1 · Hacker News 1

1. HF Daily Papers · Published Nov 4, 2026

   EmoReward: Self-Evolving Reward Calibration for Emotional Video Generation RL

   Group Relative Policy Optimization (GRPO) has advanced generative model post-training in verifiable domains such as code and mathematics, but evaluating video quality, especially emotional expression, often depends on subjective aesthetics and has no uniquely correct target. Existing approaches typically rely on fixed rewards for visual quality, motion quali

2. arXiv · Published Oct 29, 2026

   From Solver Trajectories to Teaching Trajectories: Cognition-Aligned Reasoning Distillation

   arXiv:2610.36445v1 Announce Type: new Abstract: Knowledge distillation from large reasoning models (LRMs) often uses raw teacher trajectories as supervision. However, this practice creates a mismatch between the goal of distillation and the nature of teacher trajectories. Advanced LRMs typically improve their reasoning capabilities through reward-driven post

   View source record →

3. Hacker News · Published Oct 15, 2026

   Forensic Analysis of RL/RLHF-Induced Privilege Escalation in AI Agents

   View source record →

Review priority 45 / 100

This score orders research candidates. It is not search volume, a forecast or verified commercial demand.

• Evidence

  12.7 / 30

  Distinct dated articles or first observations; repeat crawling adds nothing.

• Publisher diversity

  8 / 25

  Conservative owner grouping; syndicated copies count once.

• Comparable change

  0 / 20

  Comparable complete weeks only; no percentage from a zero baseline.

• Recent evidence

  15 / 15

  Latest publication or first observation, not repeated fetch time.

• Name specificity

  9.7 / 10

  Explicit entities and technical concepts outrank ambiguous acronyms.

Limitations and interpretation

• First observation is a collection milestone, not proof that a word or product is new to the world.
• Counts describe monitored public sources, not the whole web or search demand.
• First observed here is not the invention date of a name or idea.
• Candidates come from public titles, summaries and entity URLs, not verified full-text analysis.
• Owner grouping is conservative; undisclosed syndication may remain.
• Repeat ranking appearances are observations, not new articles or independent endorsements.
• An established concept or common product name retained as a baseline, not a newly invented term.
• Both weeks need adequate, comparable collection coverage.
• Sources behind this term changed or lack a comparable baseline.
• The previous week is too small for a reliable percentage change.
• Current evidence comes from one publisher or owner.