Turn-level Multiscale Density Ratio Estimation for LLM Agents

简体中文

L1 turn weighting and asymmetric token updates, not a live multi-turn agent environment.

Paper information

Field	Details
Paper	Turn-level Multiscale Density Ratio Estimation for LLM Agents
Institution	Alibaba Group (first author).
First public release	2026-09-15 (arXiv v1)
Author code	No public author repository found as of 2026-09-16.
Local adapter or method key	tlm-dre
Local reproduction code	src/auto_research/post_training/latest_20260916.py

Paper method and evidence

Allocate weights at multiple scales by conversation turn, and use asymmetric token updates for positive and negative density ratios.

flowchart LR
  R[Turn-level rollouts] --> W[Assign multiscale turn weights]
  W --> A[Asymmetric positive and negative token updates]

Original paper figure

Figure 1 shows the multiscale credit path. From the original paper; copyright remains with the authors.

Core equation

Local reference exposes weights and state transitions so adjacent methods can be compared under the same fixture budget.

Reported results

Paper capability, speed, and cost claims are not local effects or online projections.

Local reproduction

Three-seed mechanism results are diagnostic_only=true. Fixed-budget fixtures verify the defining operator and auditable intermediate states; they are not a benchmark, production result, or capability-ranking entry.