arXiv — Machine Learning · · 3 min read

MemoryWalker: Stop Training Agents on Contexts They Never Saw

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Computer Science > Machine Learning

arXiv:2609.00865 (cs)
[Submitted on 1 Sep 2026]

Title:MemoryWalker: Stop Training Agents on Contexts They Never Saw

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Abstract:Production agent harnesses such as Claude Code and Qwen-Agent compress context during rollout, but training under compression creates a conditioning problem: every eviction branches the effective history, so the learning object is a tree rather than a sequence. Existing linearizations either retain the rightmost path, causing time-travel leakage, or replay a depth-first traversal, causing train-inference mismatch. We introduce two exact, gradient-equivalent corrections: LogitTree, a segmented K-forward traversal, and a packed 4D attention mask. LogitTree requires K+1 backward passes; the 4D mask requires a custom kernel and white-box eviction records. We also propose SDCC (Self-Distillation for Conditioning Consistency), a single-backward-pass variational relaxation. At each eviction, it minimizes forward KL between the compressed student and a stop-gradient teacher on the reconstructed pre-eviction prefix. A residual per-junction KL of epsilon_KL gives an O(sqrt(epsilon_KL)) bound on the train-deployment total-variation gap. SDCC also applies to black-box harnesses. On seven web-search benchmarks with TC-RAG, AgentFold, MemexRL, Claude Code, and OpenCode, naive training inflates the train-rollout log-probability gap, especially on eviction-heavy batches. The exact methods stay at the no-compression floor, and SDCC substantially closes the gap, with lower logit drift and higher rollout rewards.
Comments: Your Memory-Compressing Harness Makes Training and Inference Inconsistent
Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL)
Cite as: arXiv:2609.00865 [cs.LG]
  (or arXiv:2609.00865v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2609.00865
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Zinco J [view email]
[v1] Tue, 1 Sep 2026 08:01:27 UTC (2,306 KB)
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