Exact policy gradients lose to cross-entropy by ignoring future learning
Source published ·Modelwire updated
Original coverage: arXiv cs.LG ↗·How Modelwire adds context
The development
A new arXiv paper challenges a foundational assumption in reinforcement learning: that exact policy gradients are optimal. The authors argue that standard gradient descent is myopic, optimizing only immediate accuracy gains while ignoring how each update positions future learning. They propose that cross-entropy loss implicitly captures this longer-term value by accounting for remaining learning capacity. This insight has direct implications for LLM post-training and policy-gradient methods across RL systems, suggesting that seemingly suboptimal loss functions may actually encode patience and foresight that exact gradients miss.
Modelwire’s AI-generated summary of coverage from arXiv cs.LG.
Modelwire analysis
ExplainerOur AI-generated reading of the wider context and the next developments to watch.
The paper's core claim is not just that cross-entropy loss works well empirically, but that it encodes a principled trade-off between immediate accuracy and future learning capacity. This reframes loss function choice as an implicit planning problem, not a convenience.
This connects directly to the credit assignment bottleneck explored in 'My FAULT' (Oct 1) and 'Learning What to Remember' (Sept 29), both of which tackle how to measure value across time horizons in LLM training. Where those papers solve credit assignment through explicit mechanisms (self-diagnosis, memory gain measurement), this work suggests standard losses may already embed foresight. It also echoes the finding from 'Finetuning with Sampling' (Oct 1) that conventional hierarchies between training methods obscure what's actually optimal. The implication: practitioners tuning post-training pipelines may be fighting against loss functions that already encode the right long-term incentives.
If teams applying this insight to LLM post-training report that they can match or exceed RL-based gains using cross-entropy loss alone (without policy gradient methods) on the same reasoning benchmarks used in recent RLHF papers, the thesis holds. If gains collapse on held-out domains or require careful data curation, the 'patience' encoded in cross-entropy may be dataset-specific rather than universal.
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Coverage behind this analysis
These archive entries ground the connection in our analysis. They are ordered by source publication date, with links to our coverage and the original sources.
·arXiv cs.CL
New method teaches language models which memories matter most
Researchers tackle a fundamental bottleneck in long-context language models: determining which information deserves persistent storage across extended interactions. Memory Gain Policy Optimization (MGPO) solves the credit-assignment problem by measuring how individual memory updates contribute to downstream task performance, turning delayed utility into actionable training signals. The approach isolates marginal value gains from memory rewrites, enabling…
MentionsarXiv
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Modelwire summarizes, we don’t republish. arXiv cs.LG originally reported this story as “Planning to Learn”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.