arXiv AI
Let Credit Follow Computation: Architecture-Aware Credit Transport for Large Language Model Reinforcement Learning
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arXiv Machine Learning
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Hindsight-Privileged RLVR: Single-Rollout Token Credit Without Group Sampling
arXiv:2610.20497v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) trains language models from checked task outcomes, but assigning a terminal reward to individual...
arXiv Machine Learning
Sep 23
PACT: From Credit Assignment to Critic Alignment
arXiv:2609.26355v1 Announce Type: new Abstract: Reinforcement learning has become a central component of large language model (LLM) post-training, yet token-level credit lacks a generally accepted ma...
arXiv Machine Learning
Oct 7
VETTA: Coordinating Turn- and Token-Level Credit Assignment for Multi-Turn LLM Agents
arXiv:2610.08402v1 Announce Type: new Abstract: Multi-turn LLM agents often receive sparse task feedback across several interactions, while generating each response token by token. This creates two r...
arXiv AI
Oct 15
LLM Value Functions for Credit Assignment in Agentic RL
arXiv:2610.15840v1 Announce Type: cross Abstract: Reinforcement learning (RL) for large language model (LLM) agents remains challenging in long-horizon tasks with sparse outcome rewards, where assign...
arXiv AI
Sep 18
EPIG-Tree: Compute-Optimal Branching for Gradient-Efficient Reinforcement Learning
EPIG-Tree proposes a compute‑optimal branching strategy for gradient‑efficient reinforcement learning, arguing that branches should be placed where they most reduce policy‑gradient uncertainty per unit of compute. By deriving allocation laws from a law‑of‑total‑variance decomposition, the method introduces an EPIG‑Tree score that guides branch placement using already computed rollouts, estimating occupancy‑ and score‑weighted value uncertainty. Empirical results show EPIG‑Tree reduces gradient MSE in cloned‑state control, improves frozen‑LLM gradient calibration, and outperforms flat GRPO and entropy branching in both single‑turn math and multi‑turn Wordle tasks.
arXiv AI
Sep 30
Targeting Pivotal Decisions for Credit Assignment in Agentic Reinforcement Learning
arXiv:2609.36178v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) has become a promising approach for training large language model agents. However, its uniform assignment o...