The authors introduce Symbolic Closure Analysis, a theoretical framework for reasoning spaces in which each step must satisfy local constraints.
Structural guidance improves language models’ long-horizon reasoning under sparse rewards
SAGE narrows unproductive reasoning branches and supplies depth-sensitive signals using algebraic sparsification and hyperbolic state embeddings.
Top University
Xinyue Zeng · Jiawei Zhang · Yujun Yan · Dawei Zhou
Virginia Tech · University of Wisconsin Madison · Dartmouth College
Research Digest··2 min read
Zeng et al.
Why this paper
From Dartmouth College and 2 others · Released code · Part of Credit Assignment in Agentic RL, now 20 papers
In one line
SAGE mitigates exploration and compounding biases in long-horizon reasoning via algebraic sparsification and hyperbolic embedding.
What it released
Code
What we could check
- ✓Code link in the paper (github.com)
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ✓Reports numbers on named benchmarks
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
§