PBSD: Privileged Bayesian Self-Distillation for Long-Horizon Credit Assignment

• URL: http://arxiv.org/abs/2606.09348v1
• Date: Mon, 08 Jun 2026 11:20:58 GMT
• Title: PBSD: Privileged Bayesian Self-Distillation for Long-Horizon Credit Assignment
• Abstract summary: Long-horizon agentic tasks pose a fundamental credit assignment challenge for outcome-base reinforcement learning.<n>We propose PBSD (Privileged Bayesian Self-Distillation), a Bayes-calibrated self-distillation method for fine-grained credit assignment under sparse final rewards.
• Score: 35.978805768172656
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-horizon agentic tasks pose a fundamental credit assignment challenge for outcome-base reinforcement learning: trajectory-level rewards verify final correctness but provide limited guidance on which intermediate reasoning steps or tool interactions contribute to the outcome. The difficulty is especially pronounced in multi-turn search agents, where successful trajectories may contain misleading actions and failed trajectories may contain valuable evidence-gathering steps. We propose PBSD (Privileged Bayesian Self-Distillation), a Bayes-calibrated self-distillation method for fine-grained credit assignment under sparse final rewards. PBSD measures trajectory quality through the posterior-to-prior probability ratio of the verified answer and applies Bayes' rule to convert this hard-to-estimate answer-side ratio into a tractable likelihood ratio between a standard student model and a privileged answer-conditioned teacher model. Autoregressive decomposition of this Bayesian evidence score yields turn-level signals that identify whether each intermediate turn supports or undermines the verified outcome. Consequently, PBSD provides a principled and elegant reweighting scheme that transforms sparse outcome supervision into Bayes-calibrated turn-level credit signals, while remaining fully compatible with standard policy optimization. Experiments demonstrate that PBSD consistently enhances performance across both in-domain and out-of-domain settings, and effectively transfers knowledge from short-context training to long-context inference, suggesting that its fine-grained credit assignment mechanism facilitates more effective policy learning and yields improved generalization.

Related papers

• PGPO: Potential-Guided Policy Optimization for Multi-Turn Agentic Tasks [41.82007055765541]
  We propose Potential-Guided Policy Optimization for multi-turn agentic tasks.<n>It estimates empirical state potentials from anchor-state-group return statistics within each rollout group.<n>It then derives action advantages from potential differences between adjacent states, enabling cross-trajectory credit propagation.
  arXiv  Detail & Related papers  (2026-09-02T07:44:16Z)
• Reconciling Process Supervision with Outcome-Based Credit in Agentic Policy Optimization [13.354353071601379]
  We introduce TASPO, which converts privileged supervision into outcome-grounded action credit.<n>Across three agentic benchmarks, TASPO improves over GRPO by 10.6% and generalizes better to unseen tasks.
  arXiv  Detail & Related papers  (2026-08-31T16:51:50Z)
• AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning [58.76655851910778]
  AgentOPSD is a critic-free, recursive method for turn-level credit assignment in agentic reinforcement learning.<n>We evaluate AgentOPSD on ALFWorld, WebShop, and Search-QA using Qwen2.5 models at two scales (3B and 7B)
  arXiv  Detail & Related papers  (2026-08-06T13:00:59Z)
• H$^2$SD: Hybrid Hindsight Self-Distillation [61.71131241473028]
  Reinforcement learning with verifiable rewards (RLVR) provides reliable outcome supervision for language model reasoning.<n>Existing self-distillation methods add a privileged teacher but typically assign it a fixed role.<n>We introduce Hybrid Hindsight Self-Distillation ($mathrmH2mathrmSD$), which jointly adapts teacher context and update strategy to trajectory correctness.
  arXiv  Detail & Related papers  (2026-07-21T10:47:27Z)
• TRIAGE: Role-Typed Credit Assignment for Agentic Reinforcement Learning [10.917273110894469]
  Agentic reinforcement learning requires assigning credit to environment-facing actions.<n>Standard GRPO uses the final verifier outcome as a uniform advantage over all action tokens.<n>We propose TRIAGE, a role-typed credit assignment framework that adds a semantic role axis to outcome credit.
  arXiv  Detail & Related papers  (2026-06-30T17:48:07Z)
• Rethinking Reward Supervision: Rubric-Conditioned Self-Distillation [60.55792673956761]
  We propose textbfRubric-Conditioned Self-Distillation, a framework that incorporates rubrics as structured, fine-grained feedback for on-policy self-distillation.<n>We evaluate on a diverse suite of science reasoning benchmarks and results show that rubric-conditioned self-distillation effectively converts rubric-level criteria into token-level guidance.
  arXiv  Detail & Related papers  (2026-06-17T17:54:04Z)
• Localizing Credit at the Divergence: Path-Conditioned Self-Distillation for LLM Reasoning [12.363923974156636]
  Reinforcement learning from verifiable rewards assigns a single scalar to each rollout.<n>On-policy self-distillation addresses this by letting the same model act as a teacher conditioned on privileged information.<n>We propose Hindsight Self-Distillation (HSD), which conditions the teacher on a successful peer rollout.
  arXiv  Detail & Related papers  (2026-06-14T03:37:27Z)
• TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning [52.67756371231985]
  Reinforcement learning with verifiable rewards (RLVR) is a promising approach for enhancing reasoning and agentic behavior in large language models.<n>We introduce Tree Rollout Allocation for Contrastive Exploration (TRACE), a unified rollout allocation framework.<n>Technically, TRACE allocates rollout budget to both prompt roots and intermediate prefixes that are most likely to yield mixed terminal rewards.
  arXiv  Detail & Related papers  (2026-06-09T17:16:03Z)
• VeriGate: Verifier-Gated Step-Level Supervision for GRPO [51.26100506256885]
  Group Relative Policy Optimization is an effective recipe for training reasoning models with verifier-based outcome rewards.<n>We present VeriGate, a verifier-gated extension of GRPO that addresses these limitations with three design choices.<n>We show that VeriGate improves average accuracy by about 20% and 12% for 1.5B and 7B models respectively.
  arXiv  Detail & Related papers  (2026-05-28T18:20:32Z)
• PiCA: Pivot-Based Credit Assignment for Search Agentic Reinforcement Learning [11.926589875842359]
  Large Language Model (LLM)-based search agents have significantly improved the performance of knowledge-intensive tasks.<n>Existing methods encounter critical challenges in long-horizon credit assignment.<n>We propose Pivot-Based Credit Assignment (PiCA) to reformulate the search trajectory as a sequential process of cumulative search progress.
  arXiv  Detail & Related papers  (2026-05-10T03:21:47Z)
• Self-Induced Outcome Potential: Turn-Level Credit Assignment for Agents without Verifiers [26.97849381770806]
  Self-Induced Outcome Potential treats semantic clusters of final answers as latent future outcome states for potential-based turn-level credit assignment.<n>We formalize the framework, characterize its supervised gold-answer limit, and show that SIOP improves average performance over verifier-free outcome-level baselines.
  arXiv  Detail & Related papers  (2026-05-06T14:38:48Z)
• Reducing Credit Assignment Variance via Counterfactual Reasoning Paths [11.514388061694268]
  Reinforcement learning for multi-step reasoning with large language models (LLMs) often relies on sparse terminal rewards.<n>We introduce a counterfactual comparison-based credit assignment framework, which samples multiple reasoning trajectories under the same input.<n>We construct an implicit process-level advantage estimator that transforms sparse terminal rewards into step-sensitive learning signals.
  arXiv  Detail & Related papers  (2026-04-20T13:33:54Z)
• Verified Critical Step Optimization for LLM Agents [67.05296684575445]
  Critical Step Optimization focuses preference learning on verified critical steps.<n>Method starts from failed policy trajectories rather than expert demonstrations.<n>Experiments on GAIA-Text-103 and XBench-DeepSearch show that CSO achieves 37% and 26% relative improvement over the SFT baseline.
  arXiv  Detail & Related papers  (2026-02-03T11:41:02Z)
• P2S: Probabilistic Process Supervision for General-Domain Reasoning Question Answering [51.04492568024515]
  We introduce Probabilistic Process Supervision (P2S), a novel framework for fine-grained process rewards.<n>P2S provides fine-grained process rewards without requiring a separate reward model or human-annotated reasoning steps.
  arXiv  Detail & Related papers  (2026-01-28T14:35:20Z)
• Step Potential Advantage Estimation: Harnessing Intermediate Confidence and Correctness for Efficient Mathematical Reasoning [25.562101968892833]
  Reinforcement Learning with Verifiable Rewards (RLVR) elicits long chain-of-thought reasoning in large language models (LLMs)<n>Existing approaches improve RLVR via token-level entropy or sequence-level length control, but lack a semantically grounded, step-level measure of reasoning progress.<n>We propose Step Potential Advantage Estimation (SPAE), a fine-grained credit assignment method that amplifies potential gains, penalizes potential drops, and applies penalty after potential saturates to encourage timely termination.
  arXiv  Detail & Related papers  (2026-01-07T11:36:01Z)
• BAPE: Learning an Explicit Bayes Classifier for Long-tailed Visual Recognition [78.70453964041718]
  Current deep learning algorithms usually solve for the optimal classifier by emphimplicitly estimating the posterior probabilities.<n>This simple methodology has been proven effective for meticulously balanced academic benchmark datasets.<n>However, it is not applicable to the long-tailed data distributions in the real world.<n>This paper presents a novel approach (BAPE) that provides a more precise theoretical estimation of the data distributions.
  arXiv  Detail & Related papers  (2025-06-29T15:12:50Z)
• Intra-Trajectory Consistency for Reward Modeling [67.84522106537274]
  We develop an intra-trajectory consistency regularization to enforce that adjacent processes with higher next-token generation probability maintain more consistent rewards.<n>We show that the reward model trained with the proposed regularization induces better DPO-aligned policies and achieves better best-of-N (BON) inference-time verification results.
  arXiv  Detail & Related papers  (2025-06-10T12:59:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.