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Reinforcement Learning with Verifiable Rewards (RLVR) powers modern reasoning LLMs by translating post-interaction scalar outcomes into optimization signals. However, in group-relative objectives (e.g., GRPO), reward signals vanish completely when all rollouts within a group share the same score (such as widespread mutual failures), discarding rich diagnostic feedback regarding task requirements and failure modes. In smaller models (such as 2B parameters), up to 98% of rollout groups result in universal failure, leaving standard RLVR stranded at 0% task success. Salesforce AI Research introduces Self-Retrospection Distillation (SRD, arXiv:2610.08077, repo: github.com/SalesforceAIResearch/SRD). SRD instantiates prospective learning, using privileged post-hoc trajectories to supervise pre-interaction foresight representations within the same policy without adding inference latency. Across 10 tool-integrated reasoning and long-horizon agentic benchmarks, SRD delivers gains of up to 24.2 percentage points over baseline RLVR and self-distillation. Critically, in 2B model regimes where 98% of groups are all-failure and standard RLVR flatlines at 0.0%, adding SRD reaches a 60.6% success rate under identical rollout budgets.

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Enabling LLM agents to distill and reuse modular skills from past task traces allows policies and skillbanks to co-evolve over time. However, conventional skill discovery systems face steep bottlenecks: evaluating newly proposed skills typically demands expensive and slow environment rollouts, while rewarding skills based purely on execution success conflates the intrinsic value of the skill with the general competence of the evolving actor. In newly published research (arXiv:2610.10164), researchers introduce UniSkill (repo: github.com/LimOkii/UniSKill). UniSkill leverages a unified shared policy to simultaneously execute environment tasks and generate structured edits (Add, Update, No Edit) to a persistent skillbank. Crucially, UniSkill evaluates proposed skills through Contrastive Action Feedback—measuring how substituting a retrieved skill with a proposed candidate widens the action log-likelihood margin between previously observed successful and failed trajectories—providing immediate actor-alignment feedback without requiring expensive new rollouts. Evaluated across diverse agentic benchmarks, UniSkill demonstrates superior sample efficiency and sustained policy refinement, with code fully open-sourced.

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