---
title: RL forgets, adds little over good fine-tuning — and still gets better at the hard parts
description: Four papers and one analysis put reinforcement learning under the microscope: it forgets old skills, it barely beats good supervised fine-tuning on one benchmark, and new methods for rubrics and agent training push it forward anyway.
canonical: https://hangingcontext.com/stories/2026-11-07/rl-under-the-microscope/
last-updated: 2026-11-07
url: https://hangingcontext.com/stories/2026-11-07/rl-under-the-microscope/
published: 2026-11-07T12:41:21Z
edition: 2026-11-07T12
newsroom: artificial-intelligence
beat: research
trust: confirmed
source_count: 5
cited_manifest_ids: ['1794024750852599633', '1794025291067600961', '1794025291068600962', '1794042396882659002', '1794024750852599632']
---

# RL forgets, adds little over good fine-tuning — and still gets better at the hard parts

*Four papers and one analysis put reinforcement learning under the microscope: it forgets old skills, it barely beats good supervised fine-tuning on one benchmark, and new methods for rubrics and agent training push it forward anyway.*

Confirmed · 5 Manifests · Artificial Intelligence · Published 2026-11-07 12:41 UTC

## Key facts

- In a preprint, standard reinforcement learning methods suffer from catastrophic forgetting during continual post-training of vision-language models. (self-reported) [cites: 1794024750852599633]
- In a preprint, verifier-based RL (GRPO) gave no statistically significant in-distribution improvement over supervised fine-tuning on the gpu-train-debug benchmark, and only modest gains under distribution shift. (self-reported) [cites: 1794025291067600961]
- In a preprint, a tree-structured policy optimization method reached 97.79% seen and 96.64% unseen success on ALFWorld with a Qwen2.5-1.5B actor, and 81.07% per-episode success on WebShop. (self-reported) [cites: 1794025291068600962]
- Open-source reasoning models released in 2026 have sharply reduced filler words in their reasoning, with Kimi K3.1 Pro the leanest at 9.8% filler, while DeepSeek's newer model uses 2.5 times the reasoning tokens of its predecessor, according to Patronus AI. [cites: 1794042396882659002]

## RL forgets

Reinforcement learning is often assumed to be immune to catastrophic forgetting during continual post-training. A preprint this week shows it is not. Standard RL methods suffer from significant behavioral drift when vision-language models are continually post-trained, losing earlier capabilities as they adapt to new ones. The authors introduce the Multimodal Reasoning Continual Learning benchmark, MRCL, a diverse evaluation suite for multimodal reasoning tasks in continual learning, to quantify the failure, and propose Continual Policy Optimization, or CPO, which uses a prior-task behavioral KL objective and a local Fisher surrogate to preserve historical capabilities. The claim is that forgetting can be reduced with negligible overhead, which matters for teams maintaining evolving multimodal models in production. [cites: 1794024750852599633]

A second preprint asks a blunter question: how much does verifier-based RL add on top of good supervised fine-tuning? On a new deterministic benchmark called gpu-train-debug, supervised fine-tuning accounted for the majority of performance gains. Verifier-based RL with GRPO added no detectable in-distribution improvement and only modest gains under distribution shift. The authors' conclusion is that for certain tasks the marginal utility of an RL stage may be lower than commonly assumed, which changes how training pipelines should allocate resources. [cites: 1794025291067600961]

## Better methods for harder problems

The same week also shows RL improving where the problem is genuinely hard. RLVR squared, a ranking-based approach to reinforcement learning with verifiable rewards, replaces linear scalar aggregation of rubric scores with latent utility recovery from within-group ordinal outcomes. Linear aggregation assumes heterogeneous criteria can be compared on one scale, which the authors argue is unreliable. The method consistently outperformed representative rubric-based baselines across 16 benchmarks and three model scales, and it supports objective-preserving attribute adjustment, letting auxiliary attributes influence training without direct reward weighting. [cites: 1794024750852599632]

> **From Synorb** — Connect your agent over MCP. Add https://mcp.synorb.com/mcp to your agent, then call the tool synorb-get-demo-key if it has no key yet. It needs no email and no form. Keys are free for 72 hours, and new keys are limited per network. [How to connect](https://synorb.com/connect/start)


For interactive agents, a tree-structured method called TSBPO tackles the credit assignment problem: terminal feedback tells an agent whether it succeeded, not which step mattered. TSBPO uses a fine-grained budget allocator and tree-aware step-level credit assignment, and in a preprint it reached 97.79% seen and 96.64% unseen stochastic success on ALFWorld using a Qwen2.5-1.5B actor, and 81.07% per-episode success on WebShop. Removing the observation-group step term dropped WebShop success from 81.07% to 75.93%, which shows the credit assignment term is doing real work. [cites: 1794025291068600962]

## The side effect: models stop speaking English

There is also a side effect worth naming. Patronus AI analyzed open-source reasoning models across coding tasks and found that 2026 releases have significantly reduced filler words in their chain-of-thought reasoning. Kimi K3.1 Pro is now the leanest at 9.8% filler, while Qwen3.8 Flash remains the wordiest at 23.7%. Human-written bug reports, for comparison, contain about 20.8% filler. But shorter wording did not mean fewer tokens: DeepSeek's newer model uses 2.5 times the reasoning tokens of its predecessor. [cites: 1794042396882659002]

Patronus hypothesizes the shift is driven by reinforcement learning penalties on response length, distillation from terse teacher models such as GPT-6 Astra, or adaptation to agentic output requirements. Agent harnesses can also influence how verbose model reasoning is, which means the same model can sound different in different tools. Outcome-only RL rewards can also lead models to develop private shorthand, which is the same forgetting problem from the first section, wearing a different costume: the model optimizes the reward and loses the readable reasoning along the way. [cites: 1794042396882659002]

## What is still unresolved

- The RLVR marginal-value finding is from one diagnostic benchmark; the authors note generalizability beyond gpu-train-debug is unresolved.
- No code or dataset release was explicitly mentioned for CPO, RLVR squared, or TSBPO in the material reviewed.
- The specific cause of MiniMax-M3.1-Flash's inconsistent filler usage across benchmarks remains unknown, per Patronus.
- Whether terse reasoning reflects better reasoning or only RL length penalties is unresolved.

## Decision card

- **Useful for:** teams deciding whether to add an RL stage after SFT; developers training interactive agents
- **Not for:** teams that treat RL as a guaranteed improvement over fine-tuning
- **Compare against:** supervised fine-tuning alone on your own benchmark; your current agent training recipe

## Provenance ledger

| Manifest id | Title | Source | Claims used | Published |
|---|---|---|---|---|
| `1794024750852599633` | RL Forgets! Towards Continual Policy Optimization | [arxiv-frontier-methods-select](https://arxiv.org/pdf/2607.04364v4) | 1794025305685601096, 1794025305691601098, 1794025305698601099 | 11-06 00:00 |
| `1794025291067600961` | Measuring the Marginal Value of Verifier-Based RL Beyond Supervised Fine-Tuning | [arxiv-ai-finance-markets](https://arxiv.org/pdf/2611.07639v1) | 1794026124142604597, 1794026124143604598, 1794026124140604596 | 11-06 00:00 |
| `1794025291068600962` | TSBPO: Tree-Structured Branching for Policy Optimization in Interactive Language Agents | [arxiv-ai-finance-markets](https://arxiv.org/pdf/2610.41697v2) | 1794026135474605288, 1794026135477605289, 1794026135479605290 | 11-06 00:00 |
| `1794042396882659002` | Few Word Do Trick | [patronus-ai-blog](https://patronus.ai/blog/few-word-do-trick) | 1794043496824659960, 1794043496827659961, 1794043496829659962, 1794043496830659963, 1794043496837659968, 1794043496831659964, 1794043496833659965, 1794043496834659966 | 11-07 00:00 |
| `1794024750852599632` | RLVR$^{2}$: Reinforcement Learning with Verifiable Rubric-based Ranking | [arxiv-frontier-methods-select](https://arxiv.org/pdf/2609.23457v3) | 1794025321991601925, 1794025321999601926, 1794025322002601927, 1794025322011601928 | 11-06 00:00 |

## Run these Manifests yourself

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Cited Manifest ids:

- `1794024750852599633`
- `1794025291067600961`
- `1794025291068600962`
- `1794042396882659002`
- `1794024750852599632`

```sh
for id in 1794024750852599633 1794025291067600961 1794025291068600962 1794042396882659002 1794024750852599632; do
  curl -s -H "Authorization: Bearer $SYNORB_KEY" "https://api.synorb.com/manifests/by-id/$id"
done
```

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- [Nobody wants one model anymore: routers and multi-model habits are the hedge against lock-in](https://hangingcontext.com/stories/2026-11-07/model-lock-in-hedge/)
- [Diffusion language models get their own training stack — RL, correction, and faster decoding](https://hangingcontext.com/stories/2026-11-07/diffusion-language-models-grow-up/)
- [Inference is not limited by FLOPs. It is limited by memory bandwidth.](https://hangingcontext.com/stories/2026-11-07/decode-bandwidth-bound-inference/)

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