Signals 4 · free daily AI digest

TRACE: Training Reasoning Agents for Causal Exploration with Synthesized Rewards

Paper recorded by Signals 4 on 2026-09-09 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-09 on arXiv · recorded by Signals 4 on 2026-09-10

Category: cs.AI · 人工智能 · first seen 2026-09-10

Abstract

Reinforcement learning with verifiable rewards (RLVR) has advanced language-model reasoning in domains such as mathematics and code, where objective answers are inexpensive to check. Diagnostic reasoning over complex data lacks this advantage: establishing the true cause of an anomaly often requires costly expert investigation and may remain ambiguous after the fact. We ask whether this asymmetry

Read on arXiv →

#136 most recent of 300 cs.AI papers we have recorded · ↑ newer: From Symbolic Perception to Logical Deduction: A Framework for Guiding · ↓ older: One Loop, Two Gains: Can Active Learning win the Lottery for Free?
Cite this page: TRACE: Training Reasoning Agents for Causal Exploration with Synthesized Rewards: the #136 most recent of 300 cs.AI papers we have recorded (as of 2026-09-09). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/trace-training-reasoning-agents-for-causal-exploration-with-synthesized-rewards.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.AI papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions