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VICT: Verifier-Instrumented Credit Tracing for Long-Horizon LLM Agent Reinforcement Learning

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Merged summary

TL;DR - VICT improves credit assignment for long-horizon LLM agent reinforcement learning by tracing a terminal verifier’s structured checks back to the actions that supported them. This enables finer-grained training signals without learned critics, process labels, extra rollouts, or inference-time verifier access.

  • Exposes executable or evidence-backed verifier “atoms” and links them to actions through dependency-valid proof edges.
  • Redistributes group-relative advantage only along supported edges while preserving the original terminal reward and abstaining when evidence is ambiguous.
  • Modifies only the training-time advantage tensor, avoiding additional inference-time requirements.
  • On ALFWorld and WebShop, it substantially outperforms outcome-only training and performs competitively with recent fine-grained credit-assignment methods.

Sources (1)

VICT: Verifier-Instrumented Credit Tracing for Long-Horizon LLM Agent Reinforcement Learning

arXiv cs.LG Pengcheng Li, Zhengyang Zhang, Dongxu Zhang, Sui Huang, Shaohua Ma 2026-08-28 arXiv:2608.28128
Public signals Hugging Face upvotes 1
Providers: Hugging Face · Upvotes 1 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-25 14:25:52.789523 UTC

TL;DR - VICT improves credit assignment for long-horizon LLM agent reinforcement learning by tracing a terminal verifier’s structured checks back to the actions that supported them. This enables finer-grained training signals without learned critics, process labels, extra rollouts, or inference-time verifier access.

  • Exposes executable or evidence-backed verifier “atoms” and links them to actions through dependency-valid proof edges.
  • Redistributes group-relative advantage only along supported edges while preserving the original terminal reward and abstaining when evidence is ambiguous.
  • Modifies only the training-time advantage tensor, avoiding additional inference-time requirements.
  • On ALFWorld and WebShop, it substantially outperforms outcome-only training and performs competitively with recent fine-grained credit-assignment methods.
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